Exploration of Machine Learning Models for Prediction of Gene Electrotransfer Treatment Outcomes by Alex Otten, Michael Francis, Anna Bulysheva

“…This study elucidates areas where predictive ML algorithms may ideally inform GET study design to accelerate optimization and improve efficiencies upon the further training of these models.…”
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Achieving comprehensive water productivity improvement: A multi-objective simulation-optimization model for water productivity-oriented irrigation water management by Gang Li, Chenglong Zhang, Zailin Huo, Yanqi Liu

“…In this study, a multi-objective simulation-optimization model is established for maximizing irrigation water productivity (IWP), economic water productivity (EWP), and nutritional water productivity (NWP) concurrently. …”
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Development of weighted residual RNN model with hybrid heuristic algorithm for movement recognition framework in ambient assisted living by Mustufa Haider Abidi, Hisham Alkhalefah, Zeyad Almutairi

“…For achieving a better training and testing process, the weights in the RRNN model are optimally selected by using the hybrid algorithm named Hybrid Rat Swarm with Coati Optimization Algorithm, which is developed with the integration of the Rat Warm Optimization and Coati Optimization. …”
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Simulation study on the urban-rural integration circulatory mechanism system in China: Based on system dynamics model and multi-objective genetic algorithm by Gaoyang Liang, Mingqiang Xing, Jianqiang Zhao

“…Additionally, multi-objective optimization solutions are proposed using a Multi-Objective Genetic Algorithm (MOP-GA), which suggest that a comprehensive development strategy that balances urban-rural resource allocation achieves the highest level of integration. …”
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Modeling methylene blue removal using magnetic chitosan carboxymethyl cellulose multiwalled carbon nanotube composite with genetic algorithms and regression techniques by Mahmood Yousefi, Saeid Fallahizadeh, Yosra Maleki, Amir Sheikhmohammadi, Alieh Rezagholizade-shirvan

“…From the feature importance X2 (initial methylene blue concentration) was the most important feature while X1 (contact time) and X4 (adsorbent amount) were not too important and can be eliminated from the models. The result of Genetic Algorithm analysis also proved the model has converged to the optimal solution effectively, the best solution of X1 = 49.41, X2 = 110.62, X3 = 11.85 and X4 = 20 which gives the maximum removal efficiency = 94.64% of methylene blue. …”
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Optimal load frequency control system for two-area connected via AC/DC link using cuckoo search algorithm by Gaber EL-SAADY, Alexey MIKHAYLOV, Nora BARANYAI, Mahrous AHMED, Mahmoud HEMEIDA

“…Results: The CSA was compared with particle swarm optimization algorithm (PSO) under identical conditions. …”
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Improving Forest Above-Ground Biomass Estimation Accuracy Using Multi-Source Remote Sensing and Optimized Least Absolute Shrinkage and Selection Operator Variable Selection Method by Er Wang, Tianbao Huang, Zhi Liu, Lei Bao, Binbing Guo, Zhibo Yu, Zihang Feng, Hongbin Luo, Guanglong Ou

“…Compared to the optimal SGBoost model with the Lasso variable selection method (R² of 0.69, RMSE of 18.63 Mg/ha), the VIF-Lasso method improves R² by 0.06 and reduces RMSE by 2.15 Mg/ha, while the Lasso-GA method improves R² by 0.04 and reduces RMSE by 1.93 Mg/ha. …”
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The prediction of karst-collapse susceptibility levels based on the ISSA-ELM integrated model by Jiaxin Wang, Ying Yang, Xian Yang, Yulong Lu, Yang Liu, Da Hu, Yongjia Hu

“…To address the limitations of conventional prediction methods, in this study, we introduce an enhanced predictive model, the improved sparrow search algorithm-optimized extreme learning machine (ISSA-ELM), for accurate karst-collapse susceptibility assessment. …”
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Using artificial intelligence methods for the optimal synthesis of reversible networks by Taras Kyryliuk, Mykhailo Palahuta, Vitaly Deibuk

“…The ant colony optimization algorithms were improved, including several changes to the proposed data representation model, structure, and operational principles of the synthesis algorithm, enabling effective synthesis of devices on the NCT basis along with Fredkin gates. …”
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An optimal neural network to design generators and stabilizers for multi-machine power systems based on a promoted firefly algorithm by Xiujun Nie, Nan Sun, Buqin Wang, Ganbar Akbari

“…The network has been optimized by a new promoted version of the firefly algorithm for PSS design and the parameters of this controller in a number of specific working conditions in a multi-machine power system. …”
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PSO-FDM (Particle Swarm Optimization-Finite Difference Method)-Based Simulation Model of Temperature and Velocity of Full-Scale Continuous Annealing Furnace by Yang Liu, Qiang Guo, Tieheng Yuan, Yingrui Han, Chao Liu, Wenquan Sun

“…To improve the prediction accuracy for different strip sizes, the PSO optimization algorithm was employed to determine the optimal heat transfer coefficient for each strip size. …”
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Do Sharpness-Based Optimizers Improve Generalization in Medical Image Analysis? by Mohamed Hassan, Aleksandar Vakanski, Boyu Zhang, Min Xian

“…These sharpness-based optimizers have shown improvements in model generalization compared to conventional stochastic gradient descent optimizers and their variants on general domain image datasets, but they have not been thoroughly evaluated on medical images. …”
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Research on the Gas Emission Quantity Prediction Model of Improved Artificial Bee Colony Algorithm and Weighted Least Squares Support Vector Machine (IABC-WLSSVM) by Lei Wang, Jinghang Li, Wenbo Zhang, Yu Li

“…In order to further accurately predict gas emission of working face, this paper proposes a prediction model of gas emission of working face based on the combination of improved artificial bee colony algorithm and weighted least squares support vector machine (IABC-WLSSAVM). …”
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KMOBPSO-Based High Reliability Controller Placement Algorithm in SDN by Jingting Xu, Hua Qu, Jihong Zhao

“…N+1 redundancy backup model was applied to controller placement problem in SDN in order to solve single node failure problem and to improve system reliability with taking system cost and latency into consideration，and the problem was abstracted as a multi-objective optimization problem. …”
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Optimal machine learning algorithms and UAV multispectral imagery for crop phenotypic trait estimation: a comprehensive review and meta-analysis by Adama Ndour, Gerald Blasch, João Valente, Bisrat Haile Gebrekidan, Tesfaye Shiferaw Sida

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Sustainable Mineral Processing Technologies Using Hybrid Intelligent Algorithms by Olga Shiryayeva, Batyrbek Suleimenov, Yelena Kulakova

“…The proposed control algorithm reduces chromium losses in tailings from 7.5% to 5.5%, while improving concentrate quality by 3–6%. …”
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Enhancing patient rehabilitation outcomes: artificial intelligence-driven predictive modeling for home discharge in neurological and orthopedic conditions by Leonardo Buscarini, Paola Romano, Elena Sofia Cocco, Carlo Damiani, Sanaz Pournajaf, Marco Franceschini, Francesco Infarinato

“…This process involved variables recoding, scaling, and the evaluation of different dataset balancing methods to optimize model performance. Following a thorough review and comparison of algorithms commonly employed in the clinical-rehabilitative field, the Random Over Sampling (ROS) technique, in combination with the Random Forest (RF) machine learning model, was selected. …”
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A dual-phase deep learning framework for advanced phishing detection using the novel OptSHQCNN approach by Srikanth Meda, Vangipuram Sesha Srinivas, Killi Chandra Bhushana Rao, Repudi Ramesh, Narasimha Rao Yamarthi

“…The final phase involves classifying the data using the Shallow hybrid quantum-classical convolutional neural network (SHQCNN) model. To improve the effectiveness of the classification approach, the hyperparameters present in the SHQCNN model are fine-tuned using the shuffled shepherd optimization algorithm (SSOA). …”
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Multi-Parameter Optimization of Angle Transmission Ratio of Steer-by-Wire Vehicle by Wenguang Liu, Suo Liu, Huajun Che, Xi Liu, Hua Ding

“…The new control strategy uses a genetic algorithm to optimize the yaw rate and lateral acceleration gain values at different speeds, and the two are weighted. …”
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Application Study on CPSO-FLN Model in the Steam Turbine Heat Rate Forecast by Jian HU, Chao LIU

“…The cloud model based adaptive weight strategy is introduced into the particle swarm algorithm such that the weight of the particle swarm algorithm can be self-adaptively adjusted to improve the global optimization performance of the PSO algorithm by taking advantage of the randomness and stability of cloud droplets. …”
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