AgrUNet: A Multi-GPU UNet Based Model for Crops Classification by Andrea Miola, Enrico Calore, Sebastiano Fabio Schifano

“…In this study, we develop AgrUNet, a scalable, fast, and reliable UNet-based architecture DL model to perform crop classification on multispectral multitemporal satellite data, implemented and optimized to run on single and multi-GPU HPC systems. …”
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Machine Learning Approach to Model Soil Resistivity Using Field Instrumentation Data by Md Jobair Bin Alam, Ashish Gunda, Asif Ahmed

“…The ability to infer these variables through a singular measurable soil property, soil resistivity, can potentially improve sub-surface characterization. This research leverages various machine learning algorithms to develop predictive models trained on a comprehensive dataset of sensor-based soil moisture, matric suction, and soil temperature obtained from prototype ET covers, with known resistivity values. …”
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AI-Assisted Design of Drain-Extended FinFET With Stepped Field Plate for Multi-Purpose Applications by Xiaoyun Huang, Hongyu Tang, Chenggang Xu, Yuxuan Zhu, Yan Pan, Dawei Gao, Yitao Ma, Kai Xu

“…This study introduces a novel DE-FinFET featuring a stepped field plate to improve overall performance of device. Moreover, within an AI-assisted design framework, predictive modeling and multi-objective optimization of the device are accomplished using Kolmogorov–Arnold Networks (KAN) and the Nondominated Sorting Genetic Algorithm (NSGA-III). …”
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Anatomical Parameter-driven Volumetric Modulated Arc Therapy Optimization in Left-sided Breast Cancer: A Machine Learning Framework for Lung Dose Prediction by Mukesh Kumar Zope, Deepali Patil, Rishi Raj, Seema Devi, Richa Madhawi

“…In addition, we established predictive models for lung doses utilizing the Least Absolute Shrinkage and Selection Operator, ridge, and linear regression algorithms while also evaluating dosimetric parameters for both the PTV and the organs at risk (OARs). …”
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GA-Synthesized Training Framework for Adaptive Neuro-Fuzzy PID Control in High-Precision SPAD Thermal Management by Mingjun Kuang, Qingwen Hou, Jindong Wang, Jianping Guo, Zhengjun Wei

“…The controller maintains stability under structural perturbations and abrupt thermal disturbances without the need for offline retuning, owing to the real-time inference capabilities of the ANFIS model. By combining global evolutionary optimization with intelligent online adaptation, this approach improves both accuracy and generalization, offering a practical and scalable solution for SPAD thermal management in demanding environments such as quantum communication, sensing, and single-photon detection platforms.…”
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Experimental acoustic study of small horizontal axis wind turbines based on computational fluid dynamics and artificial intelligence approaches by M. Sadeghi malekabadi, A.R. Davari

“…However, the extensive numerical computations required for accurate evaluation often hinder the implementation of multi-objective optimization strategies. This paper introduces an innovative approach to address this issue, leveraging a combination of neural network-based reduced order modeling and a multi-objective genetic algorithm. …”
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Classification of edible vegetable oils based on three-dimensional fluorescence spectroscopy and ISSA-SVM by ZHANG Jing, QI Guohong, CHEN Jingzhao, CAO Xiaoli, LI Lili

“…ObjectiveTo improve the classification accuracy of edible vegetable oils, an identification model based on three-dimensional fluorescence spectroscopy and ISSA-SVM was established.MethodsCombining the feature information of three-dimensional fluorescence spectroscopy, an improved sparrow search algorithm was used to optimize the parameters of the SVM model, constructing an edible vegetable oil identification method that integrates the characteristics of three-dimensional fluorescence spectroscopy information and the ISSA-SVM model.ResultsCompared with the SVM model, GA-SVM model, PSO-SVM model, and SSA-SVM model, the classification accuracy of the ISSA-SVM model for edible vegetable oils reached 100%.ConclusionThe ISSA-SVM model has higher convergence efficiency, system stability, and the ability to avoid local optimal solutions, which can effectively cope with complex and variable sample classification tasks.…”
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Research on Anomaly Detection Model for Power Consumption Data Based on Time-Series Reconstruction by Zhenghui Mao, Bijun Zhou, Jiaxuan Huang, Dandan Liu, Qiangqiang Yang

“…The prediction module uses the Autoformer algorithm to build an accurate and robust predictive model for unstable nonlinear sequences, and calculates the model residuals based on the prediction results. …”
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Leveraging ensemble convolutional neural networks and metaheuristic strategies for advanced kidney disease screening and classification by Abeer saber, Esraa Hassan, Samar Elbedwehy, Wael A. Awad, Tamer Z. Emara

“…The proposed model combines multiple DL models to improve overall performance by leveraging the strengths of different architectures, ensembles can enhance accuracy, robustness, and generalization. …”
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Advances in Miniaturized Computational Spectrometers by Qian Xue, Yang Yang, Wenkai Ma, Hanqiu Zhang, Daoli Zhang, Xinzheng Lan, Liang Gao, Jianbing Zhang, Jiang Tang

“…The reconstruction algorithms are classified into traditional and deep learning algorithms, and they are carefully analyzed based on the mathematical models required for spectral reconstruction. …”
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Development of a multi-level feature fusion model for basketball player trajectory tracking by Tao Wang

“…This study has the following two contributions. Firstly, a new model of athlete trajectory tracking is proposed, which improves the accuracy and efficiency of multi-target tracking by optimizing object detection algorithm and introducing hybrid branch network. …”
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IDHNet: Ultra‐Short‐Term Wind Power Forecasting With IVMD–DCInformer–HSSA Network by Wei Li, Lu Gao, Fei Zhang, XiaoYing Ren, Ling Qin

“…Subsequently, the Sparrow search algorithm (HSSA) is employed to optimize the parameters of the enhanced Informer deep neural network, which are then integrated into the improved Informer model. …”
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Target detection of helicopter electric power inspection based on the feature embedding convolution model. by Dakun Liu, Wei Zhou, Linzhen Zhou, Wen Guan

“…In the simulation experiment, the accuracy of the model in electric power circuit and equipment detection is improved by 30% compared with the traditional algorithm, and the inspection range is expanded by 26%. …”
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SIDDA: SInkhorn Dynamic Domain Adaptation for image classification with equivariant neural networks by Sneh Pandya, Purvik Patel, Brian D Nord, Mike Walmsley, Aleksandra Ćiprijanović

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A Particle Swarm Optimization-Based Ensemble Broad Learning System for Intelligent Fault Diagnosis in Safety-Critical Energy Systems with High-Dimensional Small Samples by Jiasheng Yan, Yang Sui, Tao Dai

“…To address the aforementioned challenge, this paper proposes an innovative IFD method for SCES that combines the particle swarm optimization (PSO) algorithm and the ensemble broad learning system (EBLS). …”
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Dynamic Inversion Method for Concrete Gravity Dam on Soft Rock Foundation by Guanglin Yin, Chaoning Lin, Taozhen Sheng, Wenbo Xue, Tongchun Li, Siyu Chen

“…To address this limitation, an improved particle swarm optimization (PSO) algorithm is developed for dynamic parameter inversion, combining real-time monitoring data with finite element modeling to evaluate the time-varying elastic modulus of the foundation. …”
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Analysis of the Impact for Mixed Traffic Flow Based on the Time-Varying Model Predictive Control by Rongjun Cheng, Haoli Lou, Qi Wei

“…The connected and automated vehicles (CAV) smoothing mixed traffic flow has gained attention, and a thorough assessment of these control algorithms is necessary. Our previous research proposed the time-varying model predictive control (TV-MPC) strategy, which considers the time-varying driving style of human driven vehicles (HDV), performing better than current baseline models. …”
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Research on intelligent computing offloading model based on reputation value in mobile edge computing by Jin QI, Hairong SUN, Kun GONG, Bin XU, Shunyi ZHANG, Yanfei SUN

“…Aiming at the problem of high-latency,high-energy-consumption,and low-reliability mobile caused by computing-intensive and delay-sensitive emerging mobile applications in the explosive growth of IoT smart mobile terminals in the mobile edge computing environment,an offload decision-making model where delay and energy consumption were comprehensively included,and a computing resource game allocation model based on reputation that took into account was proposed,then improved particle swarm algorithm and the method of Lagrange multipliers were used respectively to solve models.Simulation results show that the proposed method can meet the service requirements of emerging intelligent applications for low latency,low energy consumption and high reliability,and effectively implement the overall optimized allocation of computing offload resources.…”
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Apple Rootstock Cutting Drought-Stress-Monitoring Model Based on IMYOLOv11n-Seg by Xu Wang, Hongjie Liu, Pengfei Wang, Long Gao, Xin Yang

“…The neck part is optimized by the KFHA module (Kalman filter and Hungarian algorithm model), and the head part enhances post-processing effects through HIoU-SD (hierarchical IoU–spatial distance filtering algorithm). …”
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Photovoltaic Hosting Capacity Assessment of Distribution Networks Considering Source–Load Uncertainty by Chao Chen, Weifeng Peng, Cheng Xie, Shufeng Dong, Yibo Hua

“…The model considers system constraints and employs the sparrow search algorithm (SSA) to optimize the maximum PV hosting capacity. …”
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