Decentralized Voltage and Var Control of Active Distribution Network Based on Parameter-Sharing Deep Reinforcement Learning by Binqiao Zhang, Changlin He

“…By allowing agents to share parts of their neural network, the proposed Parameter Sharing - twin-delay deep deterministic policy gradient algorithm improves the stability and efficiency of voltage regulation. …”
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The Design, Creation, Implementation, and Study of a New Dataset Suitable for Non-Intrusive Load Monitoring by Carlos Rodriguez-Navarro, Francisco Portillo, Francisco G. Montoya, Alfredo Alcayde

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Adaptive dynamic programming-based multi-fault tolerant control of reconfigurable manipulator with input constraint by Zhenguo Zhang, Tianhao Ma, Yadan Zhao, Shuai Yu, Fan Zhou

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Advanced clustering and transfer learning based approach for rice leaf disease segmentation and classification by Samia Nawaz Yousafzai, Fahd N. Al-Wesabi, Hadeel Alsolai, Shouki A. Ebad, Inzamam Mashood Nasir, Emad Fadhal, Adel Thaljaoui

“…Also, the tent chaotic particle snow ablation optimizer is added into the learning process in order to improve the learning process and shorten the time of convergence. …”
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Advancing Spike Sorting Through Gradient‐Based Preprocessing and Nonlinear Reduction With Agglomerative Clustering by Mohammad Amin Lotfi, Fatemeh Zareayan Jahromy, Mohammad Reza Daliri

“…The feature extraction process is centered around capturing inherent variations in spike waveforms, assuming that strong signal correlations enable the extraction of optimal features. Finally, a density‐based clustering algorithm is employed for spike sorting. …”
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Training multi-layer binary neural networks with random local binary error signals by Luca Colombo, Fabrizio Pittorino, Manuel Roveri

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Time-Dependent Vehicle Routing Problem with Drones Under Vehicle Restricted Zones and No-Fly Zones by Shuo Wei, Houming Fan, Xiaoxue Ren, Xiaolong Diao

“…Compared to the genetic neighborhood search algorithm and the hybrid genetic algorithm, the improvement rates are 5.1% and 13.0%, respectively. …”
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Machine learning driven diabetes care using predictive-prescriptive analytics for personalized medication prescription by Manaf Zargoush, Somayeh Ghazalbash, Mahsa Madani Hosseini, Farrokh Alemi, Dan Perri

“…Effective and personalized treatment strategies are essential for improving patient outcomes and reducing healthcare costs. …”
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AI-Driven Predictive Maintenance in Mining: A Systematic Literature Review on Fault Detection, Digital Twins, and Intelligent Asset Management by Luis Rojas, Álvaro Peña, José Garcia

“…The findings highlight the increasing adoption of deep learning, reinforcement learning, and digital twins for anomaly detection and process optimization. Additionally, AI-driven methods are improving sensor-based data acquisition and asset management, extending equipment lifecycles while reducing failures. …”
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Enhancing Wind Turbine Efficiency: An Experimental Investigation of a Sensorless Three-Vector Finite Set Predictive Torque Control Approach for PMSG-Based Systems by Marouane Ahmed Ghodbane, Toufik Mohamed Benchouia, Mohamed Chebaani, Mohamed Becherif, Yassine Himeur, Amar Golea, Abdelmoumen Ghilani, Zakaria Alili, Shadi Atalla, Wathiq Mansoor

“…This approach does not require an anemometer, mechanical parameters, or rotor position sensors, making the system simpler, more reliable, and cost-effective. The 3V FS-PTC algorithm enhances control performance by selecting the three most optimal voltage vectors, two active voltage vectors and one zero voltage vector. …”
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An intelligent attention based deep convoluted learning (IADCL) model for smart healthcare security by J. Maruthupandi, S. Sivakumar, B. Lakshmi Dhevi, S. Prasanna, R. Karpaga Priya, Shitharth Selvarajan

“…Afterwards, optimization in the classification process is done by the SA-HHO algorithm, which provides the optimal weight values. …”
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Artificial Intelligence Meets Bioequivalence: Using Generative Adversarial Networks for Smarter, Smaller Trials by Anastasios Nikolopoulos, Vangelis D. Karalis

“…This approach proved effective across various scenarios, enhancing BE study sample sizes, reducing costs, and accelerating trial durations. This study highlights the potential of WGANs to improve data augmentation and optimize subject recruitment in BE studies.…”
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Economic management of microgrid using flexible non-linear load models based on price-based demand response strategies by Bishwajit Dey, Gulshan Sharma, P.N. Bokoro

“…This efficiency gain translates into a notable enhancement in the load factor, increasing from 0.83 to 0.86, and a significant drop in overall generation costs from $25,463 to $21,823 under optimal conditions. …”
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Mathematical model of on-demand route formation for public transport based on individual passenger requests in low-density population area by Svetlana S. Titova, Andrey V. Ostroukh

“…A mathematical model was developed that accounts for the specifics of populated areas with low population density, including uneven distribution of demand, large distances between populated areas, and limited financial resources. Various route optimization algorithms were investigated, and the most suitable method was selected for solving the stated problem. …”
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Two-sided Energy Storage Cooperative Scheduling Method for Transmission and Distribution Network Based on Multi-agent Attention-deep Reinforcement Learning by CHEN Shi, ZHU Yujie, LIU Yihong, XU Liuchao, TANG Guodeng

“…Compared to traditional methods, the convergence speed and optimization performance are significantly improved. …”
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Design and Analysis of a Hybrid MPPT Method for PV Systems Under Partial Shading Conditions by Oğuzhan Timur, Bayram Kaan Uzundağ

“…The partial shading of PV modules is one of the most crucial factors that causes the performance degradation of PV systems. …”
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Enhanced NDVI prediction accuracy in complex geographic regions by integrating machine learning and climate data—a case study of Southwest basin by Zehui Zhou, Jiaxin Jin, Bin Yong, Weidong Huang, Lei Yu, Peiqi Yang, Dianchen Sun

“…The LSKRX model demonstrated significant improvements in prediction accuracy compared to single-model approaches, with the most notable enhancement in BIAS. …”
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Estimating stomatal conductance of citrus orchard based on UAV multi-modal information in Southwest China by Quanshan Liu, Zongjun Wu, Ningbo Cui, Shunsheng Zheng, Shouzheng Jiang, Zhihui Wang, Daozhi Gong, Yaosheng Wang, Lu Zhao, Renjuan Wei

“…This study showed that combining multimodal information from low-cost UAV with the optimized machine learning algorithm can provide relatively accurate and robust estimates of citrus Gs. …”
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Beyond the Grid: GLRT-Based TomoSAR Fast Detection for Retrieving Height and Thermal Dilation by Nabil Haddad, Karima Hadj-Rabah, Alessandra Budillon, Gilda Schirinzi

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A systematic review of deep learning applications in database query execution by Bogdan Milicevic, Zoran Babovic

“…We categorize these approaches into three groups based on how such models are applied: improving performance of index structures and consequently data manipulation algorithms, query optimization tasks, and externally controlling query optimizers through parameter tuning. …”
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