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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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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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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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

“…Linear models such as PLS-DA and logistic regression demonstrated moderate classification performance (AUROC ≈ 0.60), whereas non-linear approaches, including ANN and XGBoost, exhibited marginal improvements. …”
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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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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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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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Stratified allocation method for water injection based on machine learning: A case study of the Bohai A oil and gas field by Changlong Liu, Pingli Liu, Qiang Wang, Lu Zhang, Zechao Huang, Yuande Xu, Shaojiu Jiang, Le Zhang, Changxiao Cao

“…Fourth, the optimized BP neural network model exhibits enhanced prediction accuracy, improved generalization capabilities, and superior dynamic relationship–capturing abilities. …”
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An RTM-Driven Machine Learning Approach for Estimating High-Resolution FAPAR From LANDSAT 5/7/8/9 Surface Reflectance by Guodong Zhang, Gaofei Yin, Yi Zhang, Jiangchuan Hu, Zongyan Li, Changjing Wang, Dujuan Ma, Jiangliu Xie

“…A coupled land–atmosphere RT model (RTM) and shuffled complex evolution optimization algorithm were first implemented at globally distributed VIIRS pixels for realistic simulation of land surface reflectance corresponding Landsat spectral bands and FAPAR under various conditions. …”
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Deepfake Face Detection and Adversarial Attack Defense Method Based on Multi-Feature Decision Fusion by Shanzhong Lei, Junfang Song, Feiyang Feng, Zhuyang Yan, Aixin Wang

“…The final decisions from both modules were integrated using a voting method, completing the deepfake face detection process. Ultimately, the model’s robustness was validated by generating adversarial samples using the I-FGSM algorithm and optimizing model performance through adversarial training. …”
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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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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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Exploring the Realization of Creative Dimensions within the Metaverse: The Case of Tabriz Metropolis by Saied Jaffari namvar, Hossein Nazmfar, Ibrahim Taghav

“…To ascertain the relative importance of research components, 20 experts were consulted using the preferential weighting evaluation ratio algorithm (SOARA). The weights of indicators and sub-indices were determined and ranked utilizing the Aras Gray analytical model. …”
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Machine learning applications in the analysis of sedentary behavior and associated health risks by Ayat S Hammad, Ayat S Hammad, Ali Tajammul, Ismail Dergaa, Ismail Dergaa, Ismail Dergaa, Maha Al-Asmakh, Maha Al-Asmakh

“…The review highlights the utility of various ML approaches in classifying activity levels and significantly improving the prediction of sedentary behavior, offering a promising approach to address this widespread health issue.ConclusionML algorithms, including supervised and unsupervised models, show great potential in accurately detecting and predicting sedentary behavior. …”
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Automated design framework for excavation retaining structures: Extending IFC standards and integrating BIM with geotechnical simulation by Qiwei Wan, Yuyuan Zhu, Haibin Ding, Wentao Hu, Changjie Xu

“…Key contributions include: (1) the extension of the IFC standard to support excavation retaining structures with objects like IfcBracedPit and IfcPitWall, improving interoperability between geotechnical models and BIM systems; (2) the integration of heuristic algorithms for automated optimization of deformation control parameters, reducing manual intervention; and (3) the promotion of design methodology that bypasses two-dimensional modeling and directly generates three-dimensional models, enhancing efficiency and allowing engineers to focus on high-level decision-making. …”
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