Temperature and Humidity Prediction Based on Machine Learning by Xiong Yanqi

“…Support Vector Machine(SVM), Neural Network(NN), and Random Forest(RF). to analyze their accuracy in predicting temperature and humidity. …”
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Research on Fault Diagnosis Method Based on Improved CNN by Hu Hao, Feng Fuzhou, Zhu Junzhen, Zhou Xun, Jiang Pengcheng, Jiang Feng, Xue Jun, Li Yazhi, Sun Guanghui

“…To solve these problems, a fault diagnosis method based on an improved convolutional neural network (CNN) is proposed. Based on the traditional CNN model, a variety of convolution stride modes were added to extract features of different scales of signals and expand the feature dimension. …”
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Advancing buffet onset prediction: a deep learning approach with enhanced interpretability for aerodynamic engineering by Jing Wang, Wei Liu, Hairun Xie, Miao Zhang

“…In this study, utilizing a comprehensive database of supercritical airfoils generated through numerical simulations, a convolutional neural network (CNN) model is firstly developed to perform buffet classification based on the flow fields. …”
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Characterization of Short-Term Strength Properties of Fiber/Cement-Modified Slurry by Ping Jiang, Tianhao Mao, Na Li, Liang Jia, Fang Zhang, Wei Wang

“…A formula satisfying the accuracy requirement was obtained by fitting the stress-strain curves using the back propagation (BP) neural network algorithm. Five parameters, including peak strength, failure strain, initial elastic modulus, residual strength, and energy dissipation, were used to characterize the short-term strength properties of fiber/cement-modified slurry. …”
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Effect of Process Parameters on Short Fiber Orientation along the Melt Flow Direction in Water-Assisted Injection Molded Part by Haiying Zhou, Hesheng Liu, Qingsong Jiang, Tangqing Kuang, Zhixin Chen, Weiping Li

“…The effect of six process parameters, including filling time, melt temperature, mold temperature, delay time, water pressure, and water temperature, on the SFO along the melt flow direction were studied through orthogonal experimental design, range analysis, and variance analysis. An artificial neural network was used to establish the nonlinear agent model between the process parameters and A11 representing the fiber orientation in melt flow direction. …”
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Research on Multilevel Classification of High-Speed Railway Signal Equipment Fault Based on Text Mining by Fan Gao, Fan Li, Zhifei Wang, Wenqi Ge, Xinqin Li

“…In the multilevel classification model, the single-layer classification model was designed based on stacking integrated learning idea; the recurrent neural network BiGRU and BiLSTM were used as primary learners, and the weight combination calculation method was designed for secondary learners, and k-fold cross verification was used to train the stacking model. …”
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CNN Accelerator Performance Dependence on Loop Tiling and the Optimum Resource-Constrained Loop Tiling by Chester Sungchung Park, Sungkyung Park

“…This paper analyzes the dependence of the convolutional neural network (CNN) accelerator performance on loop tiling. …”
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Enhancing Credit Risk Decision-Making in Supply Chain Finance With Interpretable Machine Learning Model by Guanglan Zhou, Shiru Wang

“…Specifically, we applied Extreme Gradient Boosting (XGBoost), Random Forest (RF), Least Squares Support Vector Machine (LSSVM) and Convolutional Neural Network (CNN) models for risk assessment. Our methodology included an ablation experiment along with utilizing Shapley Additive Explanation (SHAP) to elucidate the contribution and significance of specific risk factors. …”
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Hierarchical Deep Learning for Bearing Fault Detection in BLDC Motors Using Time-Frequency Analysis by Ahmed K. Ali, Wathiq Rafa Abed

“…This paper presents new hierarchical image-based time-frequency convolutional neural network (HTFICNN) for sorted bearing fault detection in brushless DC (BLDC) motors. …”
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An End-to-End Rumor Detection Model Based on Feature Aggregation by Aoshuang Ye, Lina Wang, Run Wang, Wenqi Wang, Jianpeng Ke, Danlei Wang

“…In this paper, a deep neural network- (DNN-) based feature aggregation modeling method is proposed, which makes full use of the knowledge of propagation pattern feature and text content feature of social network event without feature engineering and domain knowledge. …”
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Machine Learning for Ischemic Heart Disease Diagnosis Aided by Evolutionary Computing by Mohammad Alsaffar, Abdullah Alshammari, Gharbi Alshammari, Saud Aljaloud, Tariq S. Almurayziq, Fadam Muteb Abdoon, Solomon Abebaw

“…A picture database containing 92 images of electrocardiogram signals was also used in this project for the analysis of the Artificial Neural Network. After extensive research and testing by the medical community, which supported the project and provided positive feedback, a successful tool was developed. …”
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MAF-CNER : A Chinese Named Entity Recognition Model Based on Multifeature Adaptive Fusion by Xuming Han, Feng Zhou, Zhiyuan Hao, Qiaoming Liu, Yong Li, Qi Qin

“…The model uses bidirectional long short-term memory (BiLSTM) neural network to extract stroke and radical features and adopts a weighted concatenation method to fuse two sets of features adaptively. …”
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A Photovoltaic Array Fault Diagnosis Method Considering the Photovoltaic Output Deviation Characteristics by Jian Zhao, Qian Sun, Ning Zhou, Hao Liu, Haizheng Wang

“…Finally, the fault diagnosis of a PV array is realized by using the probabilistic neural network (PNN), and the effectiveness of the proposed method is verified. …”
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Multifractal Analysis and Compressive Strength Prediction for Concrete through Acoustic Emission Parameters by Zhiqiang Lv, Annan Jiang, Jiaxu Jin, Xiangfeng Lv

“…SVM prediction results using AE parameters perform higher precision than the artificial neural network (ANN). Furthermore, a significant reduction in sample size uses AE parameters to predict concrete strength.…”
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Prediction of COVID-19 Confirmed, Death, and Cured Cases in India Using Random Forest Model by Vishan Kumar Gupta, Avdhesh Gupta, Dinesh Kumar, Anjali Sardana

“…On this dataset, first, we performed data cleansing and feature selection, then performed forecasting of all classes using random forest, linear model, support vector machine, decision tree, and neural network, where random forest model outperformed the others, therefore, the random forest is used for prediction and analysis of all the results. …”
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Adaptive Navigating Control Based on the Parallel Action-Network ADHDP Method for Unmanned Surface Vessel by Zhijian Huang, Xinze Liu, Jiayi Wen, Guichen Zhang, Yihua Liu

“…The adaptive controller adopts a RBF neural network approximation based on the Lyapunov stability analysis to ensure the system stability. …”
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Deep Learning Implementation Using CNN to Classify Bali God Sculpture Pictures by Ni Luh Gede Pivin Suwirmayanti, I Wayan Budi Sentana, I Ketut Gede Darma Putra, Made Sudarma, I Made Sukarsa, Komang Budiarta

“…The technique used in Deep Learning is Convolutional Neural Network (CNN). The training process is first performed to perform the classification process, and then the testing process is performed. …”
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Nondestructive Detection of Authenticity of Thai Jasmine Rice Using Multispectral Imaging by Wei Liu, Xue Xu, Changhong Liu, Lei Zheng

“…In this study, the multispectral imaging system (405–970 nm) was used for the detection of adulteration in Thai jasmine rice combined with chemometric methods including principal component analysis (PCA), partial least squares (PLS), least squares-support vector machines (LS-SVM), and backpropagation neural network (BPNN). Three varieties of rice that were similar to Thai jasmine rice in appearance were selected to perform the classification and quantitative prediction experiments by multispectral images. …”
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EfficientNet-b0-Based 3D Quantification Algorithm for Rectangular Defects in Pipelines by Di Wu, Yong Hong, Jie Wang, Shaojun Wu, Zhihao Zhang, Yizhang Liu

“…This research introduces EffiTriDimNet (ETDN), a multi-task convolutional neural network that combines one-dimensional pipeline defect leakage detection data into a unified feature map while simultaneously measuring the three-dimensional characteristics of the defects. …”
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Low-threshold dual-polarization electro-optic nonlinear activation functions by Tao Jin, Jian Lin, Pengjun Wang, Qiang Fu, Yuhan Sun, Yi Zou, Shixun Dai, Weiwei Chen, Jun Li, Tingge Dai, Jianyi Yang

“…If the above-mentioned two nonlinear activation functions are introduced into the convolutional neural network to perform the modified National Institute of Standards and Technology handwritten digit classification task, validation accuracies of 97.3% and 96.85% will be achieved.…”
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