Dynamic scheduling strategies for cloud-based load balancing in parallel and distributed systems by Nasser S. Albalawi

“…The Least Response Time (LRT) technique for the Load Monitoring procedures will be developed once the job scheduling is complete. The Harmony Search Algorithm with Linear Regression (LR-HSA) is then used to do Distributed Computing-based Load Prediction and Adjustment. …”
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Cyclical hybrid imputation technique for missing values in data sets by Kurban Kotan, Serdar Kırışoğlu

“…Because incorrect imputation of missing data increases the error in the modeling phase and reduces the prediction performance of the model. When it comes to health, it is inevitable to choose models that show a higher success rate. …”
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Machine Learning Applied to Near-Infrared Spectra for Chicken Meat Classification by Sylvio Barbon, Ana Paula Ayub da Costa Barbon, Rafael Gomes Mantovani, Douglas Fernandes Barbin

“…Determining wavelengths relevance and selecting subsets for classification and prediction models are mandatory for the development of multispectral systems. …”
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Application of Monte Carlo Simulation (MCS) and Fuzzy Finite Element (FFEM) for Investigating the Uncertainty of Seepage in Homogeneous Earth Dams by Milad Kheiry, Farhoud Kalateh

“…The purpose of this research is to investigate the impact of uncertainty in the prediction of seepage flow through earth dams using the Fuzzy Monte Carlo Simulation (FMCS) new hybrid algorithm, which is implemented with the help of the finite element method and monte carlo simulation. …”
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Backdoor Defence for Voice Print Recognition Model Based on Speech Enhancement and Weight Pruning by Jiawei Zhu, Lin Chen, Dongwei Xu, Wenhong Zhao

“…Firstly, input samples are perturbed by superimposing various speech patterns, and the backdoor samples are determined based on the randomness (entropy value) of the prediction classes with perturbed inputs from a given deployment model (malicious or benign). …”
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Optimization Method for Transit Signal Priority considering Multirequest under Connected Vehicle Environment by Song Xianmin, Yuan Mili, Liang Di, Ma Lin

“…Aiming at reducing per person delay, this paper presents an optimization method for Transit Signal Priority (TSP) considering multirequest under connected vehicle environment, which is based on the travel time prediction model. Conventional arrival time of transit depended on the detection information and the front road state, which restricted the effect of priority seriously. …”
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Offline Single-Polarization Radar Quantitative Precipitation Estimation Based on a Spatiotemporal Deep Fusion Model by Yonghong Zhang, Shiwei Chen, Wei Tian, Guangyi Ma, Shuai Chen

“…Quantitative precipitation estimation (QPE) based on Doppler radar plays an important role in severe weather monitoring, industrial and agricultural production, and natural disaster prediction and prevention. However, the temporal and spatial variability of precipitation leads to large errors in radar estimates of mixed precipitation. …”
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Contrail altitude estimation using GOES-16 ABI data and deep learning by V. R. Meijer, V. R. Meijer, S. D. Eastham, S. D. Eastham, S. D. Eastham, I. A. Waitz, S. R. H. Barrett, S. R. H. Barrett

“…The altitude estimation algorithm outputs probability distributions for the contrail top altitude in order to represent predictive uncertainty. …”
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Multimodal ultrasound deep learning to detect fibrosis in early chronic kidney disease by Xiachuan Qin, Xiaoling Liu, Linlin Xia, Qi Luo, Chaoxue Zhang

“…The AUC of the multimodal US DL model was significantly better than that of the single-mode DL and clinical models. The DL algorithm developed using multimodal US images can effectively predict early fibrosis in patients with CKD with significantly greater accuracy than single-mode DL or clinical models.…”
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Classifying detrital zircon U-Pb age distributions using automated machine learning by Jack W. Fekete, Glenn R. Sharman, Xiao Huang

“…Applied to the North American Cordillera dataset, AutoML achieves an ∼0.91 F1 score when predicting between foreland and forearc basin tectonic settings and an ∼0.71 F1 score when predicting subbasins within these settings, outperforming both RF and R2. …”
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Chlorophyll-a in the Chesapeake Bay Estimated by Extra-Trees Machine Learning Modeling by Nikolay P. Nezlin, SeungHyun Son, Salem I. Salem, Michael E. Ondrusek

“…Our approach leverages the Extra-Trees (ET) algorithm, a tree-based ensemble method that offers predictive accuracy comparable to that of other ensemble models, while significantly improving computational efficiency. …”
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Smart estimation of protective antioxidant enzymes’ activity in savory (Satureja rechingeri L.) under drought stress and soil amendments by Amin Taheri-Garavand, Mojgan Beiranvandi, Abdolreza Ahmadi, Nikolaos Nikoloudakis

“…On the other hand, POX had a lower predictive correlation (R = 0.8737), indicating a lower capacity of the ANN system in forecasting this parameter. …”
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Comparative Analysis of Automated Machine Learning for Hyperparameter Optimization and Explainable Artificial Intelligence Models by Muhammad Salman Khan, Tianbo Peng, Hanzlah Akhlaq, Muhammad Adeel Khan

“…The study focuses on predicting the ultimate moment capacity of Ultra-High-Performance Concrete (UHPC) beams and U-shaped girders. …”
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The BRCA1 variant p.Ser36Tyr abrogates BRCA1 protein function and potentially confers a moderate risk of breast cancer. by Charita M Christou, Andreas Hadjisavvas, Maria Kyratzi, Christina Flouri, Ioanna Neophytou, Violetta Anastasiadou, Maria A Loizidou, Kyriacos Kyriacou

“…PolyPhen algorithm predicted that the BRCA1 p.Ser36Tyr VUS identified in the Cypriot population was damaging, whereas Align-GVGD predicted that it was possibly of no significance. …”
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Development of an Intelligent Tablet Press Machine for the In-Line Detection of Defective Tablets Using Machine Learning and Deep Learning Models by Sun Ho Kim, Su Hyeon Han

“…The TPM was verified by sorting defective tablets in-line using a pretrained defect-detection algorithm. <b>Results:</b> The RF model demonstrated the highest predictive accuracy at 93.7% with an Area Under the Curve (AUC) of 0.895, while the ANN model achieved an accuracy of 92.6% with an AUC of 0.878. …”
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Adaptive multi-agent reinforcement learning for dynamic pricing and distributed energy management in virtual power plant networks by Jian-Dong Yao, Wen-Bin Hao, Zhi-Gao Meng, Bo Xie, Jian-Hua Chen, Jia-Qi Wei

“…Furthermore, our approach demonstrates superior adaptability to unexpected events and mis-predictions, highlighting its potential for real-world implementation.…”
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Load forecasting of microgrid based on an adaptive cuckoo search optimization improved neural network by Liping Fan, Pengju Yang

“…The mean absolute percentage error (MAPE) of the ICS-BP forecasting model was 1.13%, which was very close to an ideal prediction model, and was 52.3, 32.8, and 42.3% lower than that of conventional BP, cuckoo search improved BP, and particle swarm optimization improved BP, respectively, and the root mean square error (RMSE), mean absolute error (MAE), and mean square error (MSE) of ICS-BP were reduced by 75.6, 70.6, and 94.0%, respectively, compared to conventional BP. …”
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Camouflage Target Detection Method with Mutual Compensation of Local-Global Features by HE Wenhao, GE Haibo

“…In the field of camouflage object detection (COD), the latest proposed methods mainly use the local features of the camouflaged target to complete the COD task. The output prediction map has problems such as rough target contours and incomplete objects. …”
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Application of support vector machine system introducing multiple submodels in data mining by Weinan Tang

“…Under the maximum data scale experiment, the research model improved prediction accuracy by 21 percentage points with an acceptable additional time cost of about 4 min only. …”
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Co-Optimization of Vibration Suppression and Data Efficiency in Robotic Manipulator Dynamic Modeling by Xiaowei Han, Kunru Wu, Nanmu Hui

“…Simulation results demonstrate that under typical motion conditions of the manipulator, the proposed method exhibits excellent capability in capturing nonlinear disturbances, maintaining joint prediction errors below 6 × 10⁻¹² N·m. This significantly improves the accuracy and robustness of the feedforward vibration suppression control. …”
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