Construction of a digital twin model for incremental aggregation of multi type load information in hybrid microgrids under integrity constraints by Yibo Lai, Libo Fan, Weiyan Zheng, Rongjie Han, Kai Liu

“…Based on these, establish a digital twin model for the incremental aggregation of multiple load information in a hybrid microgrid, and solve the model using an improved K-means algorithm to achieve continuous updating and optimization of load information. …”
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Machine Learning-Augmented Triage for Sepsis: Real-Time ICU Mortality Prediction Using SHAP-Explained Meta-Ensemble Models by Hülya Yilmaz Başer, Turan Evran, Mehmet Akif Cifci

“…<b>Background/Objectives:</b> Optimization algorithms are acknowledged to be critical in various fields and dynamical systems since they provide facilitation in identifying and retrieving the most possible solutions concerning complex problems besides improving efficiency, cutting down on costs, and boosting performance. …”
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Power Control and Voltage Regulation for Grid-Forming Inverters in Distribution Networks by Xichao Zhou, Zhenlan Dou, Chunyan Zhang, Guangyu Song, Xinghua Liu

“…An enhanced whale optimization algorithm (EWOA) is designed to complete the algorithm solution, thereby achieving the optimal system configuration, where an improved attenuation factor and position updating mechanism is proposed to enhance the EWOA’s global optimization capability. …”
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Joint classification and regression with deep multi task learning model using conventional based patch extraction for brain disease diagnosis by Padmapriya K., Ezhumalai Periyathambi

“…Through cooperative learning of several tasks, the model might make greater use of shared information and improve overall performance. …”
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Urban Land-use Features Mapping from LiDAR and Remote Sensing Images using Visual Transformer Network Model by Q. Yuan

“…Finally, it is found that the proposed algorithm is generally better than other representative methods, and the classification accuracy using remote sensing data and LiDAR is improved. …”
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AI-based model for Prediction of Power consumption in smart grid-smart way towards smart city using blockchain technology by Emran Aljarrah

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Prediction of Input–Output Characteristic Curves of Hydraulic Cylinders Based on Three-Layer BP Neural Network by Wei Cai, Yirui Zhang, Jianxin Zhang, Shunshun Guo, Rui Guo

“…In the process of model improvement, a nonlinear adaptive decreasing weight mechanism is introduced to improve the optimization accuracy of the algorithm, facilitating the search for optimal solutions. …”
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Distribution Network Fault Risk Assessment Method Considering Difference in Entropy Value of Rare Factors by HUANG Junxian, CHEN Chun, CAO Yijia, QUAN Shaoli, WANG Yi

“…This method uses K-means clustering algorithm to classify failures based on their consequences and an improved association rule mining algorithm to analyze rare environmental factors and evaluate high-risk and low-probability factors, so as to realize the quantitative analysis of the association between rare factors and risk levels. …”
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Full-Waveform Inversion of Two-Parameter Ground-Penetrating Radar Based on Quadratic Wasserstein Distance by Kai Lu, Yibo Wang, Heting Han, Shichao Zhong, Yikang Zheng

“…In this study, the Wasserstein distance is computed by using entropy regularization and the Sinkhorn algorithm to reduce computational complexity and improve efficiency. …”
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A Nonlinear Integer Programming Model for Integrated Location, Inventory, and Routing Decisions in a Closed-Loop Supply Chain by Hao Guo, Congdong Li, Ying Zhang, Chunnan Zhang, Yu Wang

“…Second, we develop a novel heuristic approach that incorporates simulated annealing into adaptive genetic algorithm to solve the model efficiently. Last, numerical analysis is presented to validate our solution approach, and it also provides meaningful managerial insight into how to improve the closed-loop supply chain under study.…”
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Energy management system design for high energy consuming enterprises integrating the Internet of Things and neural networks by Zhaolin Wang, Zhiping Zhang

“…The combination of neural network model prediction and optimization algorithms can achieve real-time monitoring, prediction, and optimization control of energy consumption. …”
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Finding a suitable chest x-ray image size for the process of Machine learning to build a model for predicting Pneumonia by Kriengsak Yothapakdee, Yosawaj Pugtao, Sarawoot Charoenkhun, Tanunchai Boonnuk, Kreangsak Tamee

“…This study focused on algorithm performance and training/testing time, evaluating the most suitable chest X-ray image size for machine learning models to predict pneumonia infection. …”
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Memory-Efficient Batching for Time Series Transformer Training: A Systematic Evaluation by Phanwadee Sinthong, Nam Nguyen, Vijay Ekambaram, Arindam Jati, Jayant Kalagnanam, Peeravit Koad

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Enhancing Performance and Stability of Wing-Alone UAV: A Comprehensive Mathematical Model and Simulation Approach Using MATLAB and Simulink by G. Ramanan, N. Rahul, V. E. Sathishkumar, Indraraj Upadhyaya

“…The novel objective of this study is enhancing the performance and stability aspects of the wing-alone UAV through code-based reports and the implementation of custom MATLAB algorithms. The wing-alone UAV demonstrated a 15% improvement in aerodynamic efficiency and a 10% reduction in overall weight compared to baseline designs. …”
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Prediction Model of Household Carbon Emission in Old Residential Areas in Drought and Cold Regions Based on Gene Expression Programming by Shiao Chen, Yaohui Gao, Zhaonian Dai, Wen Ren

“…Key influencing factors (e.g., electricity usage and heating energy consumption) were selected using Pearson correlation analysis and the Random Forest (RF) algorithm. Subsequently, a hybrid prediction model was constructed, with its parameters optimized by minimizing the root mean square error (RMSE) as the fitness function. …”
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State-of-the-Art Fault Detection and Diagnosis in Power Transformers: A Review of Machine Learning and Hybrid Methods by Lebo Dina Mashifane, Bongumsa Mendu, Bessie Baakanyang Monchusi

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A novel bivariate regression model derived from the clayton copula and the Odd Dagum-G family and its application by Julius Kwaku Adu-Ntim, Akoto Yaw Omari-Sasu, Maxwell Akwasi Boateng, Isaac Adjei Mensah

“…The models cumulative distribution function (CDF) and probability distribution function (PDF) are derived and the parameters were estimated using the maximum likelihood estimation (MLE) where the likelihood function was optimized using the Broyden-Fletcher-Goldfarb-Shannon (BFGS) algorithm.Simulation is conducted under various scenarios to validate the model’s robustness, exhibiting consistent estimators, reduced bias, and decreasing mean square errors (MSEs) with increasing sample size. …”
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Water quality prediction and carbon reduction mechanisms in wastewater treatment in Northwest cities using Random Forest Regression model by Jingjing Sun, Xin Guan, Xiaojun Sun, Xiaojing Cao, Yepei Tan, Jiarong Liao

“…The RFR algorithm integrates Bagging ensemble learning and random subspace theory to construct multiple decision trees and aggregate their predictions, thereby enhancing the model’s prediction accuracy and stability. …”
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Predictive modelling of hexagonal boron nitride nanosheets yield through machine and deep learning: An ultrasonic exfoliation parametric evaluation by Jerrin Joy Varughese, Sreekanth M․S․

“…A suite of machine learning regression models including Adaptive Boosting (AdaBoost) Regressor, Random Forest (RF) Regressor, Linear Regressor (LR), and Classification and Regression Tree (CART) Regressor, was employed alongside a deep neural network (DNN) architecture optimized using various algorithms such as Adaptive Moment Estimation (Adam), Root Mean Square Propagation (RMS Prop), Stochastic Gradient Descent (SGD), and Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS). …”
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Leveraging Thermal Infrared Imaging for Pig Ear Detection Research: The TIRPigEar Dataset and Performances of Deep Learning Models by Weihong Ma, Xingmeng Wang, Simon X. Yang, Lepeng Song, Qifeng Li

“…Overall, the TIRPigEar dataset demonstrates optimal performance when applied to the YOLOv9m algorithm. …”
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