Clinical-radiomics hybrid modeling outperforms conventional models: machine learning enhances stratification of adverse prognostic features in prostate cancer by Minghan Jiang, Minghan Jiang, Zeyang Miao, Run Xu, Mengyao Guo, Xuefeng Li, Guanwu Li, Peng Luo, Su Hu, Su Hu

“…LASSO regression selected optimal features, followed by model construction via five algorithms (logistic regression, decision tree, random forest, SVM, AdaBoost). …”
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Piecewise Model and Parameter Obtainment of Governor Actuator in Turbine by Jie Zhao, Li Wang, Dichen Liu, Jun Wang

“…Several testing functions are selected to compare genetic algorithm and particle swarm optimization algorithm (GA-PSO) with other basic intelligence algorithms. …”
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Multi-modal denoised data-driven milling chatter detection using an optimized hybrid neural network architecture by Haining Gao, Haoyu Wang, Hongdan Shen, Shule Xing, Yong Yang, Yinlin Wang, Wenfu Liu, Lei Yu, Mazhar Ali, Imran Ali Khan

“…The Ivy algorithm is employed to optimize the hyperparameters of CEEMD-SVD. …”
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Diagnosis Method for High-Speed Train Axle Box Bearing Slight Faults Based on Improved SAE and Temperature-Vibration Fusion by XU Xiao, SONG Dongli, WANG Zifan

“…［Method］ First, an AE (auto encoder) driven bearing temperature feature extraction method is designed to obtain the abnormal bearing temperature features, and EMD (empirical modal decomposition) method is used to process the vibration signal，so as to obtain the statistical features of the effective vibration IMF (intrinsic modal function). Then, by optimizing the dimensionality reduction algorithm based on SAE (stacked auto encoder), an effective fusion method of temperature-vibration features is proposed to achieve nonlinear fusion and dimensionality reduction of temperature and vibration features. …”
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Intelligent Energy-Efficient Train Trajectory Optimization Approach Based on Supervised Reinforcement Learning for Urban Rail Transits by Guannan Li, Siu Wing Or, Ka Wing Chan

“…Second, an IETTO model is established to handle uncertain disturbances in real-time train operation and generate optimal energy-efficient train trajectories online by optimizing energy efficiency and receiving supervisory information from trajectories of pre-trained TTO models. …”
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An efficient enhanced stacked auto encoder assisted optimized deep neural network for forecasting Dry Eye Disease by Steffi Rajan, Suresh Ponnan

“…The approach described here is novel because it merges chaotic maps into FS, employs SLSTM-STSA for improved classification accuracy (CA), and optimizes with the adaptive quantum rotation of the Enhanced Quantum Bacterial Foraging Optimisation Algorithm (EQBFOA). …”
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Enhanced PID for pedal vehicle force control using hybrid spiral sine-cosine optimization and experimental validation by Azrul Azim Abdullah Hashim, Nor Maniha Abdul Ghani, Mohammad Osman Tokhi

“…This study develops and validates a force feedback control system for automotive pedals utilizing an optimized PID controller using the hybrid Spiral Sine-Cosine algorithm (SSCA). …”
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Determination of Sequential Well Placements Using a Multi-Modal Convolutional Neural Network Integrated with Evolutionary Optimization by Seoyoon Kwon, Minsoo Ji, Min Kim, Juliana Y. Leung, Baehyun Min

“…This complex multi-million-dollar problem involves optimizing multiple parameters using computationally intensive reservoir simulations, often employing advanced algorithms such as optimization algorithms and machine/deep learning techniques to find near-optimal solutions efficiently while accounting for uncertainties and risks. …”
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Online Tool Wear Monitoring via Long Short-Term Memory (LSTM) Improved Particle Filtering and Gaussian Process Regression by Hui Xu, Hui Xie, Guangxian Li

“…To address these challenges, this paper presents an innovative tool wear prediction method that integrates a nonlinear mean function and a multi-kernel function-optimized GPR model combined with an LSTM-enhanced particle filter algorithm. …”
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Optimal protection coordination for directional overcurrent relays in radial distribution networks with inverter-based distributed energy resources by Jie Li, Jingtong Feng, Min Huang, Shenquan Liu, Gang Wang

“…The Pareto-front of the proposed optimal protection coordination problem is obtained by the improved multi-objective particle swarm optimization, where a time–space section evaluation utilizes the K-means clustering method to improve the computational efficiency. …”
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Optimizing Metro Passenger Flow Prediction: Integrating Machine Learning and Time-Series Analysis with Multimodal Data Fusion by Li Wan, Wenzhi Cheng, Jie Yang

“…The training set is utilized to train the model for optimal performance in predicting subway short-time passenger flow. …”
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Power Enhancement under Partial Shading Condition Using a Two-Step Optimal PV Array Reconfiguration by Mohamad Hossien Nahidan, Mehdi Niroomand, Behzad Mirzaeian Dehkordi

“…The introduced algorithm searches for all possible connections and finally identifies the most optimal solution. …”
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Traction Drive Control System for Railway Electric Rolling Stock Based on the Application of Power Factor as an Optimization Criterion by Goolak S., Gorobchenko O., Holub H., Kulbovskiy I., Petrychenko O.

“…The stated objective has been achieved through the solution of the following tasks: development of an algorithm for applying traction drive power factor as an optimization criterion, taking into account stochastic disturbance effects acting on the traction drive from the traction power supply system and mechanical load; development of a structural scheme for an optimized automatic control system of electric rolling stock traction drives, in which the proposed algorithm is implemented. …”
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Tone mapping based on variational model in gradient domain by IZhi-hong X, Lan-fei ZHAO, Chi ZHANG, Zhong-min ZHANG

“…Due to the low dynamic range image which was generated by the gradient domain high dynamic range compression algorithm contain the artificial boundaries and local detail distortions,a variational model in gradient domain was proposed to improve the performance of the traditional algorithm.First of all,a variational model in gradient domain was introduced to compress dynamic range,meanwhile details and edges were preserved simultaneously.Afterwards,the rate of convergence was improved by introducing the ideology of Gibbs sampler.Eventually,the improved method was employed to obtain the optimal solution of the variational model.Experimental results demonstrate that proposed algorithm reduces the degree of artificial boundaries,meanwhile low dynamic range image represents excellent capacity of detail preservation.Moreover,the real-time performance is guaranteed by the improved steepest descent method.…”
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The Optimal Dispatch for a Flexible Distribution Network Equipped with Mobile Energy Storage Systems and Soft Open Points by Yu Ji, Ying Zhang, Lei Chen, Juan Zuo, Wenbo Wang, Chongxin Xu

“…In this paper, a case study uses a regional road network in Chengdu coupled with an IEEE 33-node standard grid, and the model is solved using the non-dominated sorting genetic algorithm III (NSGA-III) algorithm. …”
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Collaborative Optimization of Passenger Control Strategy and Train Operation Plan with Variable Formations for a Rail Transit Network by Yonghao Yin, Jun Chen, Wenrui Zhao

“…We proposed a passenger control strategy and train operation plan collaborative optimization (PCS&TOP) model to schedule the train operation that improves the efficiency of passengers’ travel and reduces the cost of train operation for a rail transit network. …”
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Tone mapping based on variational model in gradient domain by IZhi-hong X, Lan-fei ZHAO, Chi ZHANG, Zhong-min ZHANG

“…Due to the low dynamic range image which was generated by the gradient domain high dynamic range compression algorithm contain the artificial boundaries and local detail distortions,a variational model in gradient domain was proposed to improve the performance of the traditional algorithm.First of all,a variational model in gradient domain was introduced to compress dynamic range,meanwhile details and edges were preserved simultaneously.Afterwards,the rate of convergence was improved by introducing the ideology of Gibbs sampler.Eventually,the improved method was employed to obtain the optimal solution of the variational model.Experimental results demonstrate that proposed algorithm reduces the degree of artificial boundaries,meanwhile low dynamic range image represents excellent capacity of detail preservation.Moreover,the real-time performance is guaranteed by the improved steepest descent method.…”
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Modified invasive weed optimization MPPT approach for PV system interfaced with BLDC motor for water pumping system by S. Nagaraja Rao, B. M. Kiran Kumar, B. M. Manjunatha, A. Suresh Kumar

“…Therefore, the modified invasive weed optimization (MIWO) approach integrated with P&O approach to improve performance under PSC. …”
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Optimizing Energy Efficiency in Vehicular Edge-Cloud Networks Through Deep Reinforcement Learning-Based Computation Offloading by Ibrahim A. Elgendy, Ammar Muthanna, Abdullah Alshahrani, Dina S. M. Hassan, Reem Alkanhel, Mohamed Elkawkagy

“…Given the complexity of this problem in large-scale vehicular networks, the study formulates an equivalent reinforcement learning model and employs a deep learning algorithm to derive optimal solutions. …”
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Intelligent optimization method of fracturing parameters for shale oil reservoirs in Jimsar Sag, Junggar Basin, NW China by Yunjin WANG, Fujian ZHOU, Hang SU, Leyi ZHENG, Minghui LI, Fuwei YU, Yuan LI, Tianbo LIANG

“…A policy gradient-genetic-particle swarm algorithm is designed, which can adaptively adjust the inertia weights and learning factors in the iterative process, significantly improving the optimization ability of the optimization strategy. …”
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