Medium- and Long-term Runoff Prediction Based on SMA-LSSVM by TIAN Jinghuan, LI Congxin, LI Ang

“…Medium-and long-term runoff prediction is extremely important for flood control,disaster reduction and the utilization efficiency improvement of water resources.To avoid the influence of prediction model parameters on prediction accuracy,this paper proposes a medium-and long-term runoff prediction model based on least squares support vector machine (LSSVM) optimized by the slime mold algorithm (SMA).Firstly,five standard test functions are selected to compare the simulation results of SMA and particle swarm optimization (PSO) algorithms in different dimensions.Secondly,SMA is used to optimize the penalty parameters and kernel parameters of LSSVM,and the comparison models of LSSVM and PSO-LSSVM are constructed.Finally,the models are verified with the monthly runoff of Manwan Hydropower Station Reservoir and Yingluoxia Hydrological Station as prediction examples.The results show that the mean square error of the SMA-LSSVM model is 29.26% and 7.42% lower than those of the LSSVM and PSO-LSSVM models,respectively,in the monthly runoff prediction of the Manwan station,and 32.61% and 6.61% lower,respectively,in the monthly runoff prediction of the Yingluoxia station.The proposed SMA-LSSVM model has better comprehensive prediction performance and also provides a new method for medium- and long-term runoff prediction.…”
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Synthetic Ensemble Forecasts: Operations‐Based Evaluation and Inter‐Model Comparison for Reservoir Systems Across California by Zachary P. Brodeur, William Taylor, Jonathan D. Herman, Scott Steinschneider

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Evapotranspiration Prediction Method Based on K-Means Clustering and QPSO-MKELM Model by Chuansheng Zhang, Minglai Yang

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Multi-dimensional constraint-based coal mining machine cutting path planning technology by Shuyang SONG, Shibo WANG, Yun WANG, Lijie WANG, Dongshuai SONG

“…NSGA-II algorithm is used to solve for the optimal cutting path. …”
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A Novel Approach to Faster Convergence and Improved Accuracy in Deep Learning-Based Electrical Energy Consumption Forecast Models for Large Consumer Groups by A. Jayanth Balaji, Binoy B. Nair, D. S. Harish Ram, Kuruvachan Kalluvelil George

“…However, the computational overhead in training models and identifying optimal training hyperparameters are challenging problems. …”
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Advanced machine learning techniques for predicting compressive strength and ultrasonic pulse velocity of concrete incorporating industrial by-products by Ehsan Mohsennia, Alireza Javid, Vahab Toufigh

“…A robust dataset, comprising 162 structured IBP concrete samples and 524 data points from existing literature, enabled rigorous training and validation of sophisticated ML models. Among the models tested, the CatBoost (CB) algorithm, optimized with the Whale Optimization Algorithm (WOA), exhibited outstanding predictive performance. …”
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Multi-objective optimization of sustainable cement-zeolite improved sand based on life cycle assessment and artificial intelligence [version 1; peer review: 1 approved, 2 approved... by Hossein MolaAbasi, Satinder Kaur Brar, Amin Tanhadoust, Sepideh Nasrollahpour, Omolbanin Ataee, Moncef L. Nehdi

“…Through a tuning process, the BPNN model found the optimal architecture and accurately predicted the unconfined compressive strength of cement-zeolite improved sand systems. …”
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High Quality Power Supply Service Mode Considering Service Life of Mitigation Equipment Against Voltage Sag by Pei LI, Yongjun YU, Zhiquan MA, Chongkai CAI

“…The improved genetic algorithm is used to solve the model. …”
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Enhancing SAR-ATR Systems’ Resistance to S2M Attacks via FUA: Optimizing Surrogate Models for Adversarial Example Transferability by Xiaying Jin, Shuangju Zhou, Chenyu Wang, Mingxin Fu, Quan Pan, Yang Li

“…Finally, Architecture modification phase modifies the activation functions and skip connections of the model architecture with the parameters fixed. Experimental results demonstrate that FUA can outperform SOTA methods and significantly improve the S2M transferability across various adversarial attack algorithms. …”
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Deep Reinforcement Learning-Based Distribution Network Planning Method Considering Renewable Energy by Liang Ma, Chenyi Si, Ke Wang, Jinshan Luo, Shigong Jiang, Yi Song

“…Based on the proximal policy optimization algorithm, an actor-critic-based autonomous generation and adaptive adjustment model for DNP is constructed. …”
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Advanced Cotton Boll Segmentation, Detection, and Counting Using Multi-Level Thresholding Optimized with an Anchor-Free Compact Central Attention Network Model by Arathi Bairi, Uma N. Dulhare

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Binocular Vision-Based Target Detection Algorithm by Huiguo Zhang

“…In the field of target detection, algorithms are challenged with multi-objective optimization problems in identifying detection targets, and it is also crucial to improve the recognition of small and insignificant targets. …”
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An Optimized Maximum Second-Order Cyclostationary Blind Deconvolution and Bidirectional Long Short-Term Memory Network Model for Rolling Bearing Fault Diagnosis by Jixin Liu, Liwei Deng, Yue Cao, Chenglin Wen, Zhihuan Song, Mei Liu, Xiaowei Cui

“…Initially, an adaptive golden jackal optimization (GJO) algorithm is employed to refine important CYCBD parameters. …”
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Study on Tourism Development Using CRITIC Method for Tourist Satisfaction by Xi Yang, Noor Azman Ali, Huam Hon Tat

“…This paper presents a novel approach for evaluating tourist satisfaction and developing optimized strategies by integrating the CRITIC method, deep learning with Multilayer Perceptron (MLP), and Genetic Algorithms (GA). …”
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Optimization of grading rings for 1000 kV dry-type air-core shunt reactor based on hybrid RBFNN–Kriging surrogate model by Yiqin Liu, Liang Xie, Dongyang Li, Yunpeng Liu, Kexin Liu, Gang Liu

“…First, the sparrow search algorithm is used to optimize the hyperparameters of the RBFNN. …”
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AI driven automation for enhancing sustainability efforts in CDP report analysis by Ramya Rangarajan, Tamilarasi Kathirvel Murugan, Logeswari Govindaraj, Venyaa Venkataraman, Krithik Shankar

“…The hybrid model consists of two main components: LSTM networks for predictive modeling of emission trends and GA for optimization of supply chain processes. …”
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LSTM-ANN-GA A HYBRID DEEP LEARNING MODEL FOR PREDICTIVE MAINTENANCE OF INDUSTRIAL EQUIPEMENT by Farouk Noumich, Abouchabaka Jaafar, Amrani Ayoub

“…The proposed hybrid model incorporates two deep learning architectures: long short-term memory (LSTM) and artificial neural networks (ANN), with a genetic algorithm (GA) applied as an optimization method to simultaneously optimize the parameters of the model structure. …”
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Crowding distance and IGD-driven grey wolf reinforcement learning approach for multi-objective agile earth observation satellite scheduling by He Wang, Weiquan Huang, Sindri Magnússon, Tony Lindgren, Chen Chen, Junyu Wu, Yanjie Song

“…This increased demand for complex and diverse imaging products requires addressing multi-objective optimization in practice. To this end, we propose a multi-objective agile Earth observation satellite scheduling problem (MOAEOSSP) model and introduce a reinforcement learning-based multi-objective grey wolf optimization (RLMOGWO) algorithm. …”
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Link Prediction in Social Networks Using the HTOA by Foad Asef, Vahid Majidnezhad, Mohammad-Reza Feizi-Derakhshi

“…HTOA, inspired by the physical phenomenon of heat transfer, identifies an optimal subset of topological features to feed XGBoost machine learning model. …”
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Deep learning framework based on ITOC optimization for coal spontaneous combustion temperature prediction: a coupled CNN-BiGRU-CBAM model by Xuming Shao, Wenhao Liu, Gang Bai, Yan Chen, Yu Liu, Jiahe Guang

“…Based on these variables, a deep learning framework combining an Improved Tornado Optimization with Coriolis force (ITOC) strategy and a CNN-BiGRU-CBAM model is proposed. …”
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