Forecasting air pollution with deep learning with a focus on impact of urban traffic on PM10 and noise pollution. by Martin Kostadinov, Eftim Zdravevski, Petre Lameski, Paulo Jorge Coelho, Biljana Stojkoska, Michael A Herzog, Vladimir Trajkovik

“…This study suggests employing Recurrent Neural Network (RNN) models featuring Long Short-Term Memory (LSTM) units for forecasting PM10 particle levels in multiple locations in Skopje simultaneously over a time span of 1, 6, 12, and 24 hours. …”
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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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DroidBet:event-driven automatic detection of network behaviors for Android applications by Song-jie WEI, Gao-xiang WU, Na LUO, Zhao-wei SHI, Zi-yang ZHOU

“…The most Android applications connect to Internet to communicate with the outside world.Applications’ network-related activities were reflected and described with network traffic.By analyzing and modeling network traffic of Android applications,network behaviors of Android applications could be subsequently characterized.Therefore,DroidBet:an event-driven network behavior automatic detection system was presented,to test and evaluate Android applications automatically.Firstly,a scenario simulation event library was built to simulate the events that applications may be executed in the process,so as to trigger the network behavior of the application as much as possible.Then,the test sequence based on the state transition analysis method was automatically generated,and the network behavior was dynamically collected during the application testing process.Finally,the machine learning method was used to learn and train the collected network behavior,and the network behavior model based on BP neural network was generated to detect the behavior of the unknown Android application.The experimental results show that DroidBet can effectively trigger and extract the network behavior of the application,which has the advantages of high accuracy and low resource cost.…”
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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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Cyber Security Intrusion Detection Using a Deep Learning Method by Basheer Ullah, Shafiq-ur-Rehman Massan, M. Abdul Rehman, Rabia Ali Khan

“…This paper proposes a deep neural network (DNN) for intrusion detection by the use of Kaggle NLS-KDD dataset with the highest attained accuracy of 92%. …”
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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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Imputation based wind speed forecasting technique during abrupt changes in short term scenario by Karan Sareen, Bijaya Ketan Panigrahi, Tushar Shikhola, Ravi Nath Tripathi, Ashok Kumar Rajput

“…Furthermore, the bi‐directional long‐short term memory deep learning approach is tied with convolution neural network to increase prediction accuracy and anticipating the sudden/abrupt changes in wind speed accurately. …”
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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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Forecast Global Ionospheric TEC: Apply Modified U‐Net on VISTA TEC Data Set by Zihan Wang, Shasha Zou, Hu Sun, Yang Chen

“…Taking advantage of the new neural network and the new database, our model achieves an root of the mean squared error from 1.2 TECU to 2.4 TECU as the prediction horizon increases from 1 hr to 7 days. …”
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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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A new model for lung cancer prediction based on differential evolution algorithm and effective feature selection by Amid Khatibi Bardsiri

“…The proposed approach is implemented on two lung cancer databases and achieves a good level of accuracy, which is compared with four other methods: C4.5 decision tree, neural network, Naive Bayes classifier, and logistic regression. …”
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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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