Research Progress on Machine Learning Prediction of Compressive Strength of Nano-Modified Concrete by Ruyan Fan, Ankang Tian, Yikun Li, Yue Gu, Zhenhua Wei

“…It reduces trial-and-error efforts and supports mix design optimization. Currently, machine learning is more adept at handling complicated datasets than experimental and traditional statistical models. …”
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Optimization of Railway Equipment Maintenance Plan Based on Intelligent Patrol Inspection System by MA Yunlong

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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

“…Predictive maintenance is essential for ensuring the reliability of industrial equipment and minimizing maintenance costs. However, current predictive algorithms sometimes reach their limits in terms of accuracy, necessitating continuous improvement. …”
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Integrated Approach to Optimizing Selection and Placement of Water Pipeline Condition Monitoring Technologies by Diego Calderon, Mohammad Najafi

“…Optimal placement is achieved with a k-Nearest Neighbors (kNN) model tuned with minimal topological and physical pipeline system features. …”
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Distributed denial of service (DDoS) classification based on random forest model with backward elimination algorithm and grid search algorithm by Mohamed S. Sawah, Hela Elmannai, Alaa A. El-Bary, Kh. Lotfy, Osama E. Sheta

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Optimization Method of Generation Rights Transaction Mechanism for Power System Accommodation Improvement by Haoliang XU, Panrun JIN, Jiheng JIANG, Zongxiang LU, Ying QIAO

“…The simulation results validate the effectiveness of the proposed algorithm in quick optimization and efficiency evaluation in monthly and annual scale.…”
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Vessel Traffic Flow Prediction in Port Waterways Based on POA-CNN-BiGRU Model by Yumiao Chang, Jianwen Ma, Long Sun, Zeqiu Ma, Yue Zhou

“…Aiming at the stage characteristics of vessel traffic in port waterways in time sequence, which leads to complexity of data in the prediction process and difficulty in adjusting the model parameters, a convolutional neural network (CNN) based on the optimization of the pelican algorithm (POA) and the combination of bi-directional gated recurrent units (BiGRUs) is proposed as a prediction model, and the POA algorithm is used to search for optimized hyper-parameters, and then the iterative optimization of the optimal parameter combinations is input into the best combination of iteratively found parameters, which is input into the CNN-BiGRU model structure for training and prediction. …”
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Fatigue life prediction of the drilling mast for rotary drilling rig using an improved hybrid algorithm by Heng Yang, Qing Lu, Yuhang Ren, Gening Xu, Wenxiao Guo, Qianbin Geng

“…To address the demand for accurate fatigue life prediction of the drilling mast for rotary drilling rig in engineering, an improved hybrid Aquila-African Vulture Optimization Algorithm (IAOAVOA) is proposed to optimize the BP neural network method for predicting the fatigue life of drill masts. …”
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Research on timing method for single intersection in transportation Internet of things based on improved Webster algorithm by Lin MA, Fuyang CHEN, Bin JIANG

“…Aiming at the timing signal problem of single intersection,a timing method based on the improved Webster algorithm in transportation Internet of things was proposed by combining the micro level analysis method with the macro level analysis method.In order to optimize the saturation and signal control delay,the average delay model was established.The game model and the conflict model were established with non-signal control and signal control.And the conflict analysis was carried out to illustrate that the optimal signal timing could coordinate the conflicting traffic flow.Based on the improved Webster algorithm,the shortest time required for the motor vehicle to pass the single intersection was additionally considered and a three-step optimization method for timing method at single intersection was proposed to reduce the traffic delay to ensure that the vehicle could pass effectively.The simulation results illustrate the effectiveness of the proposed solution.…”
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Research on dynamic prediction and optimization of high altitude photovoltaic power generation efficiency using GVSAO-CNN Model under 8-climate modes by Xiaoming Xiong, Heng Hu, Qiangfu Jia, Rongjian Zhang, Chongan Huang, Qingyuan Lu

“…The present study proposes a novel dynamic prediction model for high-altitude PV efficiency, namely the GVSAO-CNN, which combines the Gravity Search Optimization Algorithm (GVSAO). …”
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Improved War Strategy Optimization with Extreme Learning Machine for Health Data Classification by İbrahim Berkan Aydilek, Arzu Uslu, Cengiz Kına

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Frequency minimum inertia calculation of complex power systems based on an improved simulated annealing algorithm by Qiang Zhang, Qi Jia, Tingqi Zhang, Hui Zeng, Chao Wang, Wansong Liu

“…Then, based on the whole process of frequency response, we construct a power system minimum inertia assessment model taking into account the virtual inertia of new energy sources, and introduce an improved simulated annealing algorithm to solve the problem; the results validate the accuracy of the method through the IEEE-14 node model; the discussion section points out that this method provides a feasible solution for the inertia situational awareness of power system, which is helpful for the optimization of the operation, and also proposes that in the future we can take into account more uncertainties to improve the model and algorithm and to enhance the practicability and adaptability of this method. …”
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Optimizing RetinaNet anchors using differential evolution for improved object detection by Asaad Mohammed, Hosny M. Ibrahim, Nagwa M. Omar

“…To overcome this limitation, we present an enhancement to the RetinaNet model to improve its ability to handle variations in objects across different domains. …”
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Improved particle swarm optimization for fractional order PID control design in robotic manipulator system: A performance analysis by Gamil Ahmed, Ahmed Eltayeb, Nezar M. Alyazidi, Imil Hamda Imran, Tarek Sheltami, Sami El-Ferik

“…The controller has undergone refinement and enhancement using state-of-the-art particle swarm optimization (PSO) techniques incorporating a cost function and a representative bio-inspired algorithm. …”
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Development and optimization of an electrohydrodynamic dehydrator using ANN-GA for improved energy performance by Chakrit Suvanjumrat, Klar Kongsarai, Piyamon Phong-arom, Namnguen Chumphong, Machimontorn Promtong, Jetsadaporn Priyadumkol

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Distributed Photovoltaic Power Energy Generation Prediction Based on Improved Multi-objective Particle Algorithm by Yuanzheng Xiao, Huawei Hong, Feifei Chen, Xiaorui Qian, Ming Xu, Hanbin Ma

“…A hybrid LSTM-PSO model was created, where the LSTM network served as the core prediction model, and the improved MO-PSO algorithm optimized its hyperparameters, enhancing generalization and avoiding overfitting. …”
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Cloud-edge hybrid deep learning framework for scalable IoT resource optimization by Umesh Kumar Lilhore, Sarita Simaiya, Yogesh Kumar Sharma, Anjani Kumar Rai, S. M. Padmaja, Khan Vajid Nabilal, Vimal Kumar, Roobaea Alroobaea, Hamed Alsufyani

“…The hybrid algorithm's primary characteristic is its capacity to simultaneously fulfil multiple objectives, including reducing response times, enhancing resource efficiency, and decreasing operational costs. …”
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Algorithms for partitioning logical circuits into subcircuits by N. A. Kirienko

“…The problems of the use of partitioning algorithms to improve the quality of the circuit at the stage of technology-independent optimization are investigated. …”
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Tensor RT optimized driver drowsiness detection system using edge device by Chandramohan Dhasarathan, Sambasivam Gnanasekaran, Arnab Pattanayak, Gourav Kumar, Kartik Vig, Vaibhav Narain, K.M. Deva Narayan, Sunidhi Garg

“…The proposed approach utilizes multiple models to improve and accurately detecting driver drowsiness, with the models being InceptionV3, ResNet50, VGG-16, and MobileNetV1. …”
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Time Series Data Augmentation for Energy Consumption Data Based on Improved TimeGAN by Peihao Tang, Zhen Li, Xuanlin Wang, Xueping Liu, Peng Mou

“…Predicting the time series energy consumption data of manufacturing processes can optimize energy management efficiency and reduce maintenance costs for enterprises. …”
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