Development of Machine Learning Prediction Models to Predict ICU Admission and the Length of Stay in ICU for COVID‑19 Patients Using a Clinical Dataset Including Chest Computed Tom... by Seyed Salman Zakariaee, Negar Naderi, Hadi Kazemi-Arpanahi

“…The imbalance in the data numbers of groups was resolved using the synthetic minority over-sampling technique algorithm. Two sets of prediction models were separately developed to predict ICU admission and ICU LOSs of COVID‑19 patients. …”
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Remote Sensing Image Detection Method Combining Dynamic Convolution and Attention Mechanism by Yunfei Zhang, Ming Chen, Cong Chen

“…The improved model achieved an mAP of 83.0%, with accuracy improvements of 85.0%, 2.0%, and 5.0% compared to the baseline. …”
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A robust multi-scale clustering framework for single-cell RNA-seq data analysis by Songrun Jiang, Chunyan Wang, Qiucheng Sun, Zhi Zhang

“…To address these challenges, we introduce a new method, single-cell Multi-Scale Clustering Framework (scMSCF), which combines multi-dimensional PCA for dimensionality reduction, K-means clustering, and a weighted ensemble meta-clustering approach, enhanced by a self-attention-driven Transformer model to optimize clustering performance. scMSCF constructs an initial clustering framework using a multi-layer dimensionality reduction strategy to establish a robust consensus on clustering structure. …”
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Prediction of Shield Tunneling Attitude Based on WM-CTA Method by GAO Su, CHEN Cheng

“…[Conclusion] The experiments verify that the WM-CTA model delivers optimal prediction performance with high computational efficiency. …”
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Crop Recommendation Systems Based on Soil and Environmental Factors Using Graph Convolution Neural Network: A Systematic Literature Review by P. Ayesha Barvin, T. Sampradeepraj

“…We prioritize choosing the optimum graph-based model based on the dataset’s nature and inherent links to optimize crop management and resource allocation.…”
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A Distributed Collaborative Navigation Strategy Based on Adaptive Extended Kalman Filter Integrated Positioning and Model Predictive Control for Global Navigation Satellite System/... by Wanqiang Chen, Yunpeng Jing, Shuo Zhao, Lei Yan, Quancheng Liu, Zichang He

“…This framework predicts and optimizes each robot’s kinematic model, thereby improving the system’s collaborative operations and dynamic decision-making capabilities. …”
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Dynamic Error Compensation Control of Direct-Driven Servo Electric Cylinder Terminal Positioning System by Mingwei Zhao, Lijun Liu, Zhi Chen, Qinghua Yang, Xiaowei Tu

“…Then, a model to observe the dynamic error of the DDSEC-TPS was established using the improved beetle antennae search algorithm backpropagation neural network (IBAS-BPNN) prediction model according to the rigid–flexible deformation error theory of feed motion, and the observed dynamic error was compensated for in the vector control strategy of the DDSEC-TPS. …”
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Deadbeat-based control for MMC-HVDC power systems by Milovan Majstorović, Vaibhav Nougain, Leposava Ristić, Aleksandra Lekić

“…To enhance the grid stability and reduce the total harmonic distortion (THD) of the converter, the paper proposes development of an optimal voltage level-model predictive control (OVL-MPC) for a fast dynamic response, integrated with classical proportional–integral (PI) outer-loop control for robust steady-state performance. …”
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PENC: a predictive-estimative nonlinear control framework for robust target tracking of fixed-wing UAVs in complex urban environments by Shiji Hai, Xitai Na, Zhihui Feng, Jinshuo Shi, Qingbin Sun

“…This necessitates tracking algorithms capable of both target state estimation and prediction. …”
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YOLORM: An Advanced Key Point Detection Method for Accurate and Efficient Rotameter Reading in Low Flow Environments by Huang Yong, Xia Xing, Xiao Shengwang

“…Moreover, YOLORM exhibited significant reductions in parameter count and computational cost while maintaining or enhancing detection performance relative to state-of-the-art algorithms. Notably, compared to Hourglass, HRNet, and HigherHRNet, YOLORM reduced parameters by 99.3%, 92.9%, and 96.8%, and computational cost by 98.8%, 90.2%, and 95.3%, respectively, while concurrently improving precision and recall. …”
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Mechanism–Data Collaboration for Characterizing Sea Clutter Properties and Training Sample Selection by Wenhao Chen, Yong Zou, Zhengzhou Li, Shengrong Zhong, Haolin Gan, Aoran Li

“…These achievements highlight the importance of accurate sea clutter modeling and optimal training sample selection in improving target detection performance and ensuring the reliability of radar-based maritime surveillance.…”
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Multilayer entropy-weighted TOPSIS method and its decision-making in ecological operation during the subsidence period of the Three Gorges Reservoir by Ai Xueshan, Yu Yangxin, Liang Zhiming, Shi Xuanyu, Cao Rui, Zhang Xiaoke

“…Abstract Coordinating the downstream ecological demand and the power generation demand of hydropower stations is an important task in the operation of reservoirs, and how to evaluate the ecological satisfaction of the scheduling process is a difficult problem that needs to be solved urgently. A multi-objective optimal reservoir scheduling model was constructed to coordinate the spawning flow demand of " Four Major Chinese Carps"; The model takes the maximum power generation and the maximum membership degree of downstream river ecological water demand as the objective functions, and uses the dynamic programming multi-objective solution algorithm based on penalty factors to solve the problem, and obtains the non-inferior solution set in each scenario. …”
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FedACT: An adaptive chained training approach for federated learning in computing power networks by Min Wei, Qianying Zhao, Bo Lei, Yizhuo Cai, Yushun Zhang, Xing Zhang, Wenbo Wang

“…Finally, based on the table lookup and forwarding rules of the Segment Routing over IPv6 (SRv6) protocol, the sorting results of GAS are written into the SRv6 packet header, to control the order in which clients participate in model training. We conduct extensive experiments on two datasets of CIFAR-10 and MNIST, and the results demonstrate that the proposed algorithm offers improved accuracy, diminished communication costs, and reduced network delays.…”
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Comparative analysis of machine learning techniques in metabolomic-based preterm birth prediction by Ying-Chieh Han, Jane Shearer, Chunlong Mu, Donna M. Slater, Suzanne C. Tough, Gavin E. Duggan

“…Conclusions: Our results highlight the complexity of metabolomics-based modelling for preterm birth and support an iterative, model-driven approach for optimizing predictive accuracy in small-scale clinical datasets.…”
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Analysis of software methods for metal computed tomography artifact reduction: experimental research by A. V. Petraikin, Yu. A. Vasilev, Z. R. Artyukova, A. K. Smorchkova, D. S. Semenov, А. А. Baulin, A. A. Alikhanov, R. A. Erizhokov, O. V. Omelyanskaya

“…To reduce the noise level, as well as to increase the contrast sensitivity, the use of model iterative reconstruction technology is optimal.…”
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Convolutional transform learning based fusion framework for scale invariant long term target detection and tracking in unmanned aerial vehicles by Fatma S. Alrayes, Nazir Ahmad, Asma Alshuhail, Menwa Alshammeri, Ali Alqazzaz, Hassan Alkhiri, Jehad Saad Alqurni, Yahia Said

“…Moreover, the graph convolutional neural network (GCN) technique is employed for long-term target detection and tracking models. Finally, the hybrid nonlinear whale optimization algorithm with sine cosine (SCWOA) is implemented for the optimum choice of the hyperparameters involved in the GCN technique. …”
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Predicting Geostationary 40–150 keV Electron Flux Using ARMAX (an Autoregressive Moving Average Transfer Function), RNN (a Recurrent Neural Network), and Logistic Regression: A Com... by L. E. Simms, N. Yu. Ganushkina, M. Van derKamp, M. Balikhin, M. W. Liemohn

“…Logistic regression models were improved by the addition of multiplicative interaction and quadratic terms. …”
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Prediction of Power System Ramping Demand Using Meteorological Features by Kuan Lu, Song Gao, Jun Li, Kang Chen, Chunhao Yu

“…A Gaussian negative log-likelihood loss function is employed for training to optimize uncertainty prediction performance. Validation using 2024 and 2025 operational data from Shandong, China, indicates that the proposed model improves prediction accuracy by 18.4%, achieves a 92.5% interval coverage rate, and produces narrower prediction interval bandwidths under equivalent coverage rates.…”
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Machine Learning Techniques Applied to COVID-19 Prediction: A Systematic Literature Review by Yunyun Cheng, Rong Cheng, Ting Xu, Xiuhui Tan, Yanping Bai

“…By establishing a multi-level classification framework that included traditional statistical models (such as ARIMA), ML models (such as SVM), deep learning (DL) models (such as CNN, LSTM), ensemble learning methods (such as AdaBoost), and hybrid models (such as the fusion architecture of intelligent optimization algorithms and neural networks), it revealed that the hybrid modelling strategy effectively improved the prediction accuracy of the model through feature combination optimization and model cascade integration. …”
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A Long Distance Underwater Visible Light Communication System With Single Photon Avalanche Diode by Chao Wang, Hong-Yi Yu, Yi-Jun Zhu

“…Furthermore, the SPAD detection algorithm and the optimal detection threshold of the UVLC system are proposed. …”
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