On the Choice of System Strength Metrics for the Allocation and Sizing of Synchronous Condensers in Power Grids by Shereefdeen Oladapo Sanni, Olatunji Obalowu Mohammed, Suprava Chakraborty, Ayodele Isqeel Abdullateef, Abdulrahaman Okino Otuoze, Olumoroti Ikotun, Ephraim Bonah Agyekum

“…Although the NRSCR-based method significantly improved system performance during faults—offering faster voltage recovery post-fault and higher fault current contributions—it resulted in a cost increase of approximately 7 times. …”
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Damage prediction of rear plate in Whipple shields based on machine learning method by Chenyang Wu, Xiangbiao Liao, Lvtan Chen, Xiaowei Chen

“…The results demonstrate that the training and prediction accuracies using the Random Forest (RF) algorithm significantly surpass those using Artificial Neural Networks (ANNs) and Support Vector Machine (SVM). …”
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Flexible Combinatorial-Bids-Based Auction for Cooperative Target Assignment of Unmanned Aerial Vehicles by Tianning Wang, Zhu Wang, Wei Li, Chao Liu

“…A comparison of the results demonstrates that the FCBA can provide comparable optimal results to a genetic algorithm but has better computational efficiency, and the reconnaissance effectiveness can be improved by considering cooperative observation.…”
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Approaches for handling imbalanced data used in machine learning in the healthcare field: A case study on Chagas disease database prediction. by André G Coimbra, Cleiane G Oliveira, Matheus P Libório, Hasheem Mannan, Laercio I Santos, Elisa Fusco, Marcos F S V D'Angelo

“…The application of these techniques aims to improve the quality of life for individuals, reduce healthcare costs, and allocate available resources more efficiently, making it a preventive action.…”
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Research and Application of a Health Management Device for the Electrical Control Cabinet of the EMU by Yongshen WU, Jian FENG, Mingshuai ZHANG, Chaoyue JU, Feihong YI

“…The usage of the device can also beneficial to the intelligent maintenance of electrical cabinets and optimize maintenance costs.…”
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Convolutional neural networks and vision transformers for Plankton Classification by Loris Nanni, Alessandra Lumini, Leonardo Barcellona, Stefano Ghidoni

“…The study considers the creation of ensembles combining different Convolutional Neural Network (CNN) models and transformer architectures to understand whether different optimization algorithms can result in more robust and efficient classification across various plankton datasets. …”
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Study on Color Detection of Korla Fragrant Pears by Near-Infrared Spectroscopy Combined with PLSR by Yifan Xia, Yang Liu, Hong Zhang, Jikai Che, Qing Liang

“…The optimal detection model for the color index L* was SGCD-UVE-PLSR (correlation coefficient (R) = 0.80, Root Mean Square Error (RMSE) = 1.19); for the color index a*, it was VN-SPA-PLSR (R = 0.84 and RMSE = 1.28), and for the color index b*, it was MSC-UVE-PLSR (R = 0.84 and RMSE = 1.25). …”
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Deep reinforcement learning based online lifting path planning for tower cranes in unknown dynamic environments by Kai Wang, Jing Li, Zhiyuan Yin, Jiankang Zhang, Xin Ma

“…Moreover, a novel reward function is introduced to optimize the smoothness of the lifting path, which improves the success rate and optimizes the energy and time cost. …”
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Enhancing the e-commerce shopping experience with IoT-enabled smart carts in smart stores by Jian Wang

“…In this paper explores the application of Internet of Things (IoT) technology in retail settings, leveraging low-cost radio frequency identification (RFID) tags attached to products. …”
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Techno-economic modeling and analysis of a PV EV charged with battery energy storage system (BESS) on Kalimantan Island by Aripriharta, Satria Adiguna, Arif N. Afandi, Muhammad Cahyo Bagaskoro

“…This research analyses the optimal combination of solar panels and battery capacity in 11 cities in Kalimantan using particle swarm optimization (PSO) and grey wolf optimization (GWO) algorithms to maximize energy output, reduce levelised energy costs, and maximally reduce carbon emissions. …”
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Predicting hospital outpatient volume using XGBoost: a machine learning approach by Lingling Zhou, Qin Zhu, Qian Chen, Ping Wang, Hao Huang

“…Accurate prediction of outpatient demand can significantly enhance operational efficiency and optimize the allocation of medical resources. This study aims to develop a predictive model for daily hospital outpatient volume using the XGBoost algorithm. …”
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Calibration of the Composition of Low-Alloy Steels by the Interval Partial Least Squares Using Low-Resolution Emission Spectra with Baseline Correction by M. V. Belkov, K. Y. Catsalap, M. A. Khodasevich, D. A. Korolko, A. V. Aseev

“…Further improvement of calibration accuracy was achieved by using the adaptive iteratively reweighted penalized least squares algorithm for spectrum baseline correction. …”
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Drilling dynamics measurement of drilling motors and its application in recognition of motor operation states through machine learning by Fei Li, Haolan Song, Yifan Wang

“…Due to the increased non-productive time and drilling costs brought about by accidental damage to drilling motors, predictive maintenance for drilling motors is necessary to optimize asset utilization. …”
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ARIMA-Kriging and GWO-BiLSTM Multi-Model Coupling in Greenhouse Temperature Prediction by Wei Zhou, Shuo Liu, Junxian Guo, Na Liu, Zhenglin Li, Chang Xie

“…Utilizing the high-quality data processed by this model, this study proposes and constructs a novel Grey Wolf Optimizer and Bidirectional Long Short-Term Memory (GWO-BiLSTM) temperature prediction framework, which combines a Grey Wolf Optimizer (GWO)-enhanced algorithm with a Bidirectional Long Short-Term Memory (BiLSTM) network. …”
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Research on the Application of Artificial Intelligence in Quantitative Investment: Implementation Scenarios, Practical Challenges, and Future Trends by Chen Boshan

“…Second, the research focuses on key AI applications in quantitative investment, including multi-factor model optimization, high-frequency market risk management, multimodal data integration, and algorithmic trading enhancement. …”
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Distributed Authentication of Power Grid Safety and Stability Control Terminals Based on DHT and Blockchain by Yening LAI, Ke FENG, Tongwei YU, Wang WANG, Guanjun TANG

“…By combining the distributed Hash table (DHT) technology with the blockchain technology, a blockchain distributed storage optimization method is firstly proposed based on DHT of Skip Graph structure. …”
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Intelligent resource allocation in internet of things using random forest and clustering techniques by Nahideh Derakhshanfard, Lida Hosseinzadeh, Fahimeh Rashid Jafari, Ali Ghaffari

“…Numerous current resource allocation methods, such as evolutionary algorithms and multi-agent reinforcement learning, are grossly inefficient at adapting well to IoT networks in light of dynamic and rapid changes due to the inherent computational complexity and high cost. …”
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Efficient Material Flow and Storage Space Determination in Automated Distribution Centers by Mohammed Alnahhal, Nikola Gjeldum, Mohammed Ruzayqat

“…Items with relatively large demand levels have scenario 3 as the optimal one. Results also showed that the model reduces both total costs and stacker crane utilization while improving system flexibility.…”
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QuantumGS-Box—A Key-Dependent GA and QRNG-Based S-Box for High-Speed Cloud-Based Storage Encryption by Anish Saini, Athanasios Tsokanos, Raimund Kirner

“…Cloud computing has revolutionized the digital era by providing a more efficient, scalable, and cost-effective infrastructure. Secure systems that encrypt and protect data before it is transmitted over a network and stored in the cloud benefit the entire transmission process. …”
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Real-time temperature prediction of large-scale lithium battery module driven by data based on few measurement points by Jiajie HAN, Qingyang YUAN, Yu LI, Bo ZHANG, Ke XUE, Tian LAN

“…Proper thermal management ensures uniform heat distribution, which is essential for optimizing efficiency, safety, and reliability. However, obtaining comprehensive real-time temperature data for large-scale battery systems is challenging due to the high costs, complexity, and impracticality of deploying extensive sensor networks. …”
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