Accelerating spin Hall conductivity predictions via machine learning by Jinbin Zhao, Junwen Lai, Jiantao Wang, Yi‐Chi Zhang, Junlin Li, Xing‐Qiu Chen, Peitao Liu

“…Here, we have developed a residual crystal graph convolutional neural network (Res‐CGCNN) deep learning model to classify and predict SHCs solely based on the structural and compositional information. …”
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Abnormal neural circuits and altered brain network topological properties in patients with persistent postural-perceptual dizziness by Kangzhi Li, Xia Ling, Jing Zhao, Zhiqun Wang, Xu Yang

“…Network-based statistic results reveal an abnormal neural network in PPPD patients with key nodes in the occipital visual cortex, precuneus, sensorimotor cortex, multisensory vestibular cortex and cerebellum. …”
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A Pervasive Approach to EEG-Based Depression Detection by Hanshu Cai, Jiashuo Han, Yunfei Chen, Xiaocong Sha, Ziyang Wang, Bin Hu, Jing Yang, Lei Feng, Zhijie Ding, Yiqiang Chen, Jürg Gutknecht

“…Four classification methods (Support Vector Machine, K-Nearest Neighbor, Classification Trees, and Artificial Neural Network) distinguished the depressed participants from normal controls. …”
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An Integrated SVM-LSTM Method for VPP Resource Classification and Load Forecasting in Real-time Market Trading by He Zhao, Yucheng Hou, Zhifa Lin, Xin Cao, Xiao Yu

“…The average prediction error of the improved long short-term memory neural network algorithm is 0.35%, and the maximum error is only 0.62%. …”
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Identification of spodumene using a remote-sensing index cube from SDGSAT-1 and other satellites by Siyuan Li, Nannan Zhang, Yong Li, Li Chen, Hao Zhang, Jinyu Chang, Jintao Tao, Jianpeng Jing

“…The model combines a convolute onal neural network (CNN) and a graph convolutional network (GCN), integrating spatial and spectral features to enhance identification accuracy. …”
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Comparing Supervised Machine Learning Models for Covid-19 patient detection using a Combination of Clinical and Laboratory Dataset by Narges Mohebbi, Mehdi Tutunchian, Meysam Alavi, Mehrdad Kargari, Amir Behnam Kharazmy

“…Considering the importance and role of using clinical and laboratory data in the diagnosis of people with covid-19, in this paper K-NN, SVM, decision tree, random forest, Naive Bayes, neural network and XGBoost models are the most common machine learning models, and a dataset containing 1354 records consisting of clinical and laboratory data of patients in Imam Hossein Hospital in Tehran has been used to diagnose patients with covid-19. …”
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Optimized Application of CGA-SVM in Tight Reservoir Horizontal Well Production Prediction by Chao Wang, Ruogu Wang, Yuhan Lin, Jiafei Zhang, Xiaofei Xie, Zidan Zhao, Yunlin Xu

“…Compared with traditional support vector machine, BP neural network, KNN and naive Bayes, the improved support vector machine has a higher prediction accuracy, and the average error is only 2.7%. …”
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A Correlation Analysis-Based Structural Load Estimation Method for RC Beams Using Machine Vision and Numerical Simulation by Chun Zhang, Yinjie Zhao, Guangyu Wu, Han Wu, Hongli Ding, Jian Yu, Ruoqing Wan

“…Subsequently, a deep neural network (DNN) is trained as a FEM surrogate model to quickly predict the structural strain response by considering material uncertainties. …”
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Fine-Grained Classification via Hierarchical Feature Covariance Attention Module by Yerim Jung, Nur Suriza Syazwany, Sujeong Kim, Sang-Chul Lee

“…In the deep convolutional neural network, the covariance between feature maps positively affects the selection of features to learn discriminative regions automatically. …”
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Systematic Research on the Application of Steel Slag Resources under the Background of Big Data by Le Kang, Hui Ling Du, Hao Zhang, Wan Li Ma

“…Secondly, the steel slag prediction model based on the convolution neural network (CNN) is established. The material data of steelmaking, the operation data of steelmaking process, and the data of steel slag composition are put into the model from the Hadoop platform, and the prediction of the slag composition is further realized. …”
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Revolutionizing RIS Networks: LiDAR-Based Data-Driven Approach to Enhance RIS Beamforming by Ahmad M. Nazar, Mohamed Y. Selim, Daji Qiao

“…Our approach extends a Graph Neural Network (GNN) by integrating LiDAR-captured user locations as inputs. …”
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Optimal selection of machine learning algorithms for ciprofloxacin prediction based on conventional water quality indicators by Shenqiong Jiang, Xiangju Cheng, Baoshan Shi, Dantong Zhu, Jun Xie, Zhihong Zhou

“…The evaluation results showed that the generalized regression neural network (GRNN) model optimized by particle swarm optimization (PSO) had the best prediction among all the models under the conditions of six input variables, namely COD, NH4+-N, DO, WT, TN, and pH. …”
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Vehicle Detection and Tracking Based on Improved YOLOv8 by Yunxiang Liu, Shujun Shen

“…Then we replaced the convolutional kernel with a dual convolutional kernel to construct a lightweight deep neural network. Subsequently, the Focaler-EIoU loss function is introduced to improve the accuracy. …”
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Improving the accuracy of soil texture determination using pH and electro conductivity values with ultrasound penetration-based digital soil texture analyzer by Emre Kilinc, Umut Orhan

“…Using the Ultrasound Penetration-based Digital Soil Texture Analyzer (USTA), this research combined ultrasound time series data with pH and EC measurements to predict sand, silt, and clay ratios through machine learning methods—support vector regression (SVR), Random Forest (RF), and multi-layer perceptron neural network (MLPNN). Simulations showed that RF yielded the best results, improving R2 values to 0.52, 0.33, and 0.31 for sand, silt, and clay, respectively. …”
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Dynamic Response of a Casting Crane Rigid-Flexible Coupling System to High Temperature by Yunsheng Xin, Qing Dong, Qisong Qi, Qinglu Shi

“…The constitutive equation for the elastic modulus of Q355 alloy steel at different temperatures was predicted using test data and a neural network algorithm. Based on crane structural characteristics and the principle of system dynamics, a coupling vibration model was established that included the crane flexible girder, cabin, trolley, crane, and temperature. …”
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Distance Constrained Sparse Representation Approach for Scene Based Nonuniformity Correction in Infrared Imaging by Zhe Liu, Hongtao Deng, Xun Zhu, Longxiang Li

“…This method diverges from conventional neural-network based techniques by eschewing spatial filters for the computation of the desired image. …”
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Important Citation Identification by Exploding the Sentiment Analysis and Section-Wise In-Text Citation Weights by Shahzad Nazir, Muhammad Asif, Shahbaz Ahmad, Hanan Aljuaid, Rimsha Iftikhar, Zubair Nawaz, Yazeed Yasin Ghadi

“…The first technique is based on extracting the different sections of the research articles and performing citation count. We applied Neural Network and Multiple Regression on section-wise citations for automatic weight assignment. …”
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A Novel Deep Hybrid Recommender System Based on Auto-encoder with Neural Collaborative Filtering by Yu Liu, Shuai Wang, M. Shahrukh Khan, Jieyu He

“…To tackle these problems, some authors have considered the integration of a deep neural network to learn user and item features with traditional collaborative filtering. …”
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Winter Wheat Yield Prediction Using Satellite Remote Sensing Data and Deep Learning Models by Hongkun Fu, Jian Lu, Jian Li, Wenlong Zou, Xuhui Tang, Xiangyu Ning, Yue Sun

“…By adjusting the key parameters of the Convolutional Neural Network (CNN) with IGWO, the prediction accuracy is significantly enhanced. …”
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Combined flow prediction model for natural gas pipeline network based on EMD-Attention-GRU by Jiacheng MEN, Yuguang FAN, Lin GAO, Hongxian LIN, Ke ZHANG

“…Specifically, the model is to substitute the raw flow data of the natural gas pipeline network with its time series component obtained through Empirical Mode Decomposition (EMD), input the intrinsic mode function component obtained into the GRU neural network, calculate the attention probability weight at different times with the attention integrated into the network, and finally learn in the network and predict the time series of flow in the natural gas pipeline network. …”
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