Aquifer vulnerability assessment in data-scarce areas: a spatially explicit assessment by Changhyun Jun, Dongkyun Kim, Sayed M. Bateni, Meghdad Biyari, Ely Salwana, Farzaneh Sajedi Hosseini, Amir Mosavi, Hao-Ting Pai, Bahram Choubin

“…Supportive machine learning approaches such as bagged adaptive boosting (BAB), averaged neural network (avNNet), heteroscedastic discriminant analysis (HAD), rotation forest (RotationF), and an ensemble method were applied to assess groundwater vulnerability using k-fold cross-validation. …”
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Digital Assets and the Global Economy: How the Use of Statistical Models Can Help Bitcoin Price Prediction by L. P. Bakumenko, N. S. Vasileva

“…The LSTM neural network model on a similar data set showed a MAE error equal to 2.57%.Conclusion. …”
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ASSESSMENT OF CHANGES IN THE GROUND CHARACTERISTICS OF HAIL DURING ANACTIVE IMPACT ON HAIL PROCESSES by N. V. Teunov

“…In this work, an assessment of changes in the spectral and energy characteristics of hail is carried out during an active action according to the data of the hail-gauge network and radiosonding of the atmosphere. …”
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Technology for Improving the Accuracy of Predicting the Position and Speed of Human Movement Based on Machine Learning Models by Artem Obukhov, Denis Dedov, Andrey Volkov, Maksim Rybachok

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Hongtang Bridge Expansion Joints InSAR Deformation Monitoring with Advanced Phase Unwrapping and Mixed Total Least Squares in Fuzhou China by Baohang Wang, Wu Zhu, Chaoying Zhao, Bojie Yan, Xiaojie Liu, Guangrong Li, Wenhong Li, Liye Yang

“…Additionally, to address design matrix bias resulting from inaccurate temperature data, we employ the mixed total least squares method to estimate deformation parameters. …”
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Adaptive Autoencoder-Based Intrusion Detection System with Single Threshold for CAN Networks by Donghyeon Kim, Hyungchul Im, Seongsoo Lee

“…The proposed autoencoder model was trained exclusively on normal data. A portion of the attack data was utilized to determine the optimal detection threshold using a Gaussian kernel density estimation function, while the frame count was selected based on error rate analysis. …”
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Research on TOC Log Evaluation Method Based on PCA-BP Neural Network by SHANG Yazhou, XU Duonian, ZHANG Zhaohui, LIU Jianyu, ZHAO Wenwen

“…The method is based on the weighted average of sensitive logging curves and TOC test results as the original data set. …”
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Auto-segmentation of cerebral cavernous malformations using a convolutional neural network by Chi-Jen Chou, Huai-Che Yang, Cheng-Chia Lee, Zhi-Huan Jiang, Ching-Jen Chen, Hsiu-Mei Wu, Chun-Fu Lin, I-Chun Lai, Syu-Jyun Peng

“…Subsequently, this data was processed using a 3D convolutional neural network known as DeepMedic. …”
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Segmental Dynamic Duty Cycle Control for Sampling Scheduling in Wireless Sensor Networks by Lufeng Mo, Yujia Jiang, Guoying Wang, Jizhong Zhao

“…The experimental results using data from soil respiration monitoring sensor networks show that the proposed SDDC method can lead to better reconstructive quality compared to constant duty cycle of the same average sampling rate. …”
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Data-Driven Modeling of Systemic Air Traffic Delay Propagation: An Epidemic Model Approach by Shanmei Li, Dongfan Xie, Xie Zhang, Zhaoyue Zhang, Wei Bai

“…Through fluctuation analysis of the average flight delay based on complex network theory, we find that the long-term dynamic of airport delay is dominated by the propagation factor (PF), which reveals that the long-term dynamic of airport delay should be studied from the perspective of propagation. …”
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Nonlinear Volatility Risk Prediction Algorithm of Financial Data Based on Improved Deep Learning by Wangsong Xie

“…Then, the nonlinear volatility risk prediction model of the financial data is established through the wavelet principal component analysis noise reduction module and the long and short-term memory network (LSTM) module, and the nonlinear volatility trend is extracted from multiple financial data series to realize the nonlinear volatility risk prediction of the financial data. …”
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ECG-GraphNet: Advanced arrhythmia classification based on graph convolutional networks by Myeonghun Lee, BS, Jiwoo Lim, MS, JinKook Kim, MS

“…Objective: We propose Electrocardiogram Graph Convolutional Network (ECG-GraphNet), a graph convolutional network designed to classify arrhythmias into 3 types: normal (N), supraventricular ectopic (S), and ventricular ectopic (V) beats. …”
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Multi-task meta-attention network for traditional Chinese medicine diagnostic recommendation by YingShuai Wang, YanLi Wan, HongPu Hu, HongPu Hu

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Research on IGBT Sequentially Prediction Algorithm Based on Improved Wavelet Neural Network by Kexun HUANG, Songrong WU, Binan XIANG, Rui XU, Zhenwei TU

“…Based on the analysis of IGBT failure mechanism, with the IGBT aging data, the instantaneous collector emitter peak voltage was selected as the failure characteristic parameter, the training set and test set were constructed by the sliding time window method, and then the wavelet neural network prediction model improved by genetic algorithm was built in MATLAB for prediction, which was compared with the traditional wavelet neural network prediction model. …”
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Enhancing Risk-Adjusted EWMA Control Chart Utilizing Artificial Neural Networks by Abdullah Ali H. Ahmadini, Imad Khan, Hadeel AlQadi, Saddam Hussain

“…Our proposed approach involves creating an exponentially weighted moving average (EWMA) control chart. This graph is based on residuals derived from the ANN model allowing comprehensive analysis of actual cardiac surgery patient data. …”
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People’s Influence on Indoor Body Area Networks Channel Characteristics by Slawomir J. Ambroziak, Filipe D. Cardoso, Manuel M. Ferreira, Mariella Sarestoniemi, Luis M. Correia

“…This research is significant for the development of Body Area Networks, as a promising solution for 5G and 6G networks, namely as an emerging technology expected to revolutionize mobile healthcare via real-time monitoring and analysis of medical data. …”
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RCSAN residual enhanced channel spatial attention network for stock price forecasting by WenJie Sun, Ziyang Liu, ChunHong Yuan, Xiang Zhou, YuTing Pei, Cui Wei

“…Abstract This study proposes a stock price prediction model based on the Residual-enhanced Channel-Spatial Attention Network (R-CSAN), which integrates channel-spatial adaptive attention mechanisms with residual connections to effectively capture the multidimensional complex patterns in financial time series. …”
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Revolutionizing municipality performance evaluation: A dynamic network fusion of BSC and DEA by Yusof Chaharlang, Hamed Soleimani, Esmaeil Mehdizadeh, Alireza Alinejad

“…This study introduces an advanced performance measurement system for 31 municipalities in Tehran and Shahriar, integrating the Balanced Scorecard (BSC) and Data Envelopment Analysis (DEA) methodologies. The combination of BSC and DEA was chosen because BSC offers a multidimensional framework for assessing performance from diverse perspectives, while DEA provides a quantitative tool for evaluating efficiency, particularly useful when dealing with multiple inputs and outputs. …”
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Spatio-Temporal Graph Neural Networks for Streamflow Prediction in the Upper Colorado Basin by Akhila Akkala, Soukaina Filali Boubrahimi, Shah Muhammad Hamdi, Pouya Hosseinzadeh, Ayman Nassar

“…Using 30 years of monthly streamflow data from 20 monitoring stations, the STGNN predicted streamflow over a 36-month horizon and was evaluated against traditional models, including random forest regression (RFR), LSTM, gated recurrent units (GRU), and seasonal auto-regressive integrated moving average (SARIMA). …”
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Distributed photovoltaic cluster output monitoring method based on time series data acquisition by Hua Ye, Xuegang Lu, Wei Zhang, Fei Cheng, Ying Zhao

“…In order to obtain a reliable distributed photovoltaic cluster output monitoring method, this paper analyzes the output relationship of cluster power stations, combining time series data analysis methods for distributed cluster processing and monitoring data processing, a combined model of ceemdan and Bayesian neural network is proposed, the representative power plant prediction values obtained by establishing a combination model are weighted to obtain the cluster output prediction values. …”
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