Electricity Carbon Coupled Market Modeling Method and Market Optimization Mechanism Based on Dynamic Carbon Emission Intensity by Tong ZHAO, Xuesong LI, Hao ZHOU, Yu DING, Bin YANG, Wentao WANG, Peng WANG

“…A Markov decision iterative optimal coordination algorithm (MDIOCA) is proposed to solve the model. …”
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Enhanced insulator fault detection using optimized ensemble of deep learning models based on weighted boxes fusion by Stefano Frizzo Stefenon, Laio Oriel Seman, Gurmail Singh, Kin-Choong Yow

“…Using deep learning-based models combined with interpretative techniques can be an alternative to improve power grid inspections and increase their reliability. …”
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Using the proximal policy optimization and prospect theory to train a decision-making model for managing personal finances by Vladyslav Didkivskyi, Dmytro Antoniuk, Tetiana Vakaliuk, Yevhen Ohinskyi

“…The subject of this article is the development of a decision-making model that can, in the future, be incorporated into a personal finance simulator to improve personal finance literacy. …”
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Physics-informed modeling and process optimization of friction stir welding of AA7075-T6 with a zinc interlayer by Dejene Alemayehu Ifa, Dame Alemayehu Efa, Naol Dessalegn Dejene, Sololo Kebede Nemomsa

“…This study is the first to combine a zinc interlayer with machine learning (ML) based optimization in the FSW of AA7075-T6. Artificial Neural Networks (ANN), Support Vector Regression (SVR), Random Forest Regression (RFR), a Genetic Algorithm (GA) for optimization, and Response Surface Methodology (RSM) for statistical modeling were used to analyze a dataset of 60 observations. …”
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Tensor Network Methods for Hyperparameter Optimization and Compression of Convolutional Neural Networks by A. Naumov, A. Melnikov, M. Perelshtein, Ar. Melnikov, V. Abronin, F. Oksanichenko

“…However, challenges such as hyperparameter optimization (HPO) and model compression remain critical for improving performance and deploying models on resource-constrained devices. …”
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Personalized metronomic radiopharmaceutical therapy through injection profile optimization via physiologically based pharmacokinetic (PBPK) modeling by Aryan Golzaryan, M. Soltani, Farshad Moradi Kashkooli, Babak Saboury, Arman Rahmim

“…We designed a treatment algorithm to select optimal regimens with high AD, while investigating what we term radiopharmaceutical delivery payload (RDP). …”
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A novel network-level fused deep learning architecture with shallow neural network classifier for gastrointestinal cancer classification from wireless capsule endoscopy images by Muhammad Attique Khan, Usama Shafiq, Ameer Hamza, Anwar M. Mirza, Jamel Baili, Dina Abdulaziz AlHammadi, Hee-Chan Cho, Byoungchol Chang

“…Two novel architectures, Sparse Convolutional DenseNet201 with Self-Attention (SC-DSAN) and CNN-GRU, are fused at the network level using a depth concatenation layer, avoiding the computational costs of feature-level fusion. Bayesian Optimization (BO) is employed for dynamic hyperparameter tuning, and an Entropy-controlled Marine Predators Algorithm (EMPA) selects optimal features. …”
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Prediction of UHPC mechanical properties using optimized hybrid machine learning model with robust sensitivity and uncertainty analysis by ZhiGuang Zhou, Jagaran Chakma, Md Ahatasamul Hoque, Vaskar Chakma, Asif Ahmed

“…Each dataset was standardized and split into training (80%) and testing (20%) subsets. Hyperparameter optimization was conducted using a random search algorithm to improve prediction accuracy. …”
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Research on real time measurement model and measurement system for gas concentration in extraction drilling by Dayang Yu, Junhao Zhu, Xing Li, An He, Huaiqian Liu, Changjiang Chen, Yong Liu

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A comparative study of different kinematic wake models within metaheuristics for efficient wind farm layout optimization by Antonio J. Romero-Barrera, David Casillas-Pérez, Laura Cornejo-Bueno, Jorge Pérez-Aracil, Alvaro Paricio-Garcia, Miguel A. Lopez-Carmona, Juan Blanco-Sancho, Sujan Ghimire, Ravinesh C. Deo, Antonio J. Caamaño, Sancho Salcedo-Sanz

“…We analyze the performance of seven analytical wake models—Jensen, Park2, Frandsen, Larsen, Bastankhah, Ishihara, and Zhang—to estimate the downstream wind speed deficits included in the objective function of metaheuristics (Genetic Algorithm, Particle Swarm Optimization, and Coral Reefs Optimization with Substrate Layers), for an optimal WFLO solution. …”
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Drought Amplifies the Suppressive Effect of Afforestation on Net Primary Productivity in Semi-Arid Ecosystems: A Case Study of the Yellow River Basin by Futao Wang, Ziqi Zhang, Mingxuan Du, Jianzhong Lu, Xiaoling Chen

“…Integrating multi-source remote sensing data (2000–2020), meteorological observations with the Standardized Precipitation Evapotranspiration Index (SPEI) and an improved CASA model, this study systematically investigates spatiotemporal patterns of vegetation net primary productivity (NPP) responses to extreme drought events while quantifying vegetation coverage’s regulatory effects on ecosystem drought sensitivity. …”
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Short-Term Wind Power Prediction Model Based on PSO-CNN-LSTM by Qingquan Lv, Jialin Zhang, Jianmei Zhang, Zhenzhen Zhang, Qiang Zhou, Pengfei Gao, Haozhe Zhang

“…To improve short-term wind power prediction accuracy, this study constructs a hybrid particle swarm optimization (PSO)-CNN-LSTM model for seasonal forecasting. …”
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A Comprehensive Analysis of the Triple-Hybrid Metamodel of MLP-PSO-ARIMA for Forecasting the FDSD Index: A Case Study of Khuzestan Province by Mohammad Ansari Ghojghar

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Optimal Power Flow for High Spatial and Temporal Resolution Power Systems with High Renewable Energy Penetration Using Multi-Agent Deep Reinforcement Learning by Liangcai Zhou, Long Huo, Linlin Liu, Hao Xu, Rui Chen, Xin Chen

“…Test results demonstrate that the proposed DRL model achieves a 100% convergence and feasibility rate, with an optimal generation cost similar to that provided by MATPOWER. …”
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Prediction of CO2 emission for the central European countries through five metaheuristic optimization techniques helping multilayer perceptron by Hossein Moayedi, Azfarizal Mukhtar, Serhan Alshammari, Mohamed Boujelbene, Isam Elbadawi, Quynh T Thi, Mojtaba Mirzaei

“…To develop a reliable predictive network considering the problem complexity, multilayer perceptron (MLP) is combined with several nature-inspired optimization algorithms, namely, black hole algorithm (BHA), future search algorithm (FSA), backtracking search algorithm (BSA), biogeography-based optimization (BBO), and shuffled complex evolution (SCE). …”
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Examining Nasdaq Market Data and Presenting an Optimized Model by Extreme Gradient Boosting Regression and Artificial Bee Colony by Ali Ahmadpour

“…These optimization techniques aim to enhance the model's predictive performance by improving parameter tuning and model generalization. …”
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A Hybrid-A* Path Planning Method Based on Equal Step Hierarchical Expansion by CHEN Xinpeng, XU Biao, HU Manjiang, QIN Zhaobo, BIAN Yougang

“…This method can be applied to unstructured road scenes to plan a safe, smooth and driveable path for intelligent vehicles that meets the vehicle kinematic constraints of vehicle operation. First by improving the way of child node development and the reasonable design of the heuristic function and cost function, quick search of guarantee algorithm is a relatively smooth driving path. …”
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A MultiNeutrosophic Offset Model for Clustering and Optimizing College Students' Mental Health Literacy in Interdisciplinary Contexts by Yan Li

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Optimization of engine parameters and emission profiles through bio-additives: Insights from ANFIS Modeling of Diesel Combustion by Abbas Rohani, Javad Zareei, Kourosh Ghadamkheir, Seyed Alireza Farkhondeh

“…Various machine learning configurations and training algorithms were employed to optimize the model's performance. …”
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The different member equivalent circulating density prediction model and drilling parameter optimization under narrow density window by Wanchun Zhao, Wanchun Zhao, Zhipeng Yang, Tingting Wang, Yan Zhou, Wei Song, Jingrun Li, Peihong Zhai

“…The model uses nonlinear regression algorithms to predict ECD values for different members. …”
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