Analytical framework for household energy management: integrated photovoltaic generation and load forecasting mechanisms by Zhenping Xie, Yansha Li

“…To address the challenges associated with the intermittent and variable nature of solar energy, an innovative hybrid model is proposed. Specifically, this research integrates the K-nearest neighbor (KNN) classification method and genetic algorithm (GA) to optimize a backpropagation neural network (BPNN). …”
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A Design Framework for Scalable and Adaptive Multi-Agent Coordination in Dynamic Environments: Addressing Concurrent Agent and Environment Interactions by Raza Muhammad Kazim, Guoxin Wang, Zhenjun Ming, Jinhui Cao, Janet K. Allen, Farrokh Mistree

“…In this study, we explore the integration of surrogate response surface modeling (SRSM) with optimization algorithms, comparing Stochastic Gradient Descent (SGD) with a fixed learning rate, and adaptive learning rate (ALR) optimizers—including Adaptive Moment Estimation (ADAM) and Adaptive Approximate Direction Method Algorithm (AADMA)—to enhance MAS performance metrics, such as Agent Collision Rate (ACR), Agent Movement Frequency (AMF), and Task Completion Time (TCT). …”
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Advanced predictive analytics for bio-waste management using YOLOv8-SPP to enhance waste prediction and sustainability in smart cities by Selvalakshmi Balasubramanium, Bharathiraja Nagu, Shonak Bansal, Mohammad Rashed Iqbal Faruque, Kholoud Saad Al-mugren

“…This research suggests a new predictive analytics model using the YOLOv8-SPP algorithm for improved waste management. …”
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GIS partial discharge fault diagnosis method based on SGMD-LSTM by Zhang Yun, Zhang Chao, Zhang Shiyong, Ma Pengchi, Yang Guang, Ding Hao

“…The Osprey-Cauchy-Sparrow Search Algorithm (OCSSA) is used to adaptively optimize the number of hidden layer nodes and learning rate of LSTM. …”
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Learning atomic forces from uncertainty-calibrated adversarial attacks by Henrique Musseli Cezar, Tilmann Bodenstein, Henrik Andersen Sveinsson, Morten Ledum, Simen Reine, Sigbjørn Løland Bore

“…Abstract Adversarial approaches, which intentionally challenge machine learning models by generating difficult examples, are increasingly being adopted to improve machine learning interatomic potentials (MLIPs). …”
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Application of Machine Learning to Statistical Evaluation of Artificial Rainfall Enhancement by Li Dan, Lin Wen, Liu Qun, Feng Hongfang, Hu Shuping, Wang Zhihai

“…Each statistical model is greatly improved after six root square transformations of rainfall data. …”
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Proactive dynamic flooding regulations for river basins in China’s arid and semi-arid region of Xinjiang by Xintong Gong, Qiang Zhang, Senlin Tang, Yungang Bai, Vijay P. Singh, Zhenlin Lu

“…We used an improved pre-release constraint algorithm, such as the long-short-series mean correction method, and evaluated the flood stage potential during the aforementioned three intervals. …”
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The Inversion of SPAD Value in Pear Tree Leaves by Integrating Unmanned Aerial Vehicle Spectral Information and Textural Features by Ning Yan, Yasen Qin, Haotian Wang, Qi Wang, Fangyu Hu, Yuwei Wu, Xuedong Zhang, Xu Li

“…Finally, four machine learning methods, namely XGBoost, random forest (RF), back-propagation neural network (BPNN), and optimized integration algorithm (OIA), were used to construct inversion models of the SPAD value of pear trees, with different feature inputs based on vegetation indices, textural features, and their combinations, respectively. …”
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Research on fine-grained classification of rare metal granite lithology based on deep learning by ZHAO Hengqian, WANG Pan, LIU Zhiguo, MIAO Qunfeng, LI Zhibin, TANG Guanglong, QI Yunfei, XIE Yu, WANG Mengmeng

“…In the future, the accuracy of fine-grained classification of rare metal granite lithology can be further improved by improving the quantity and quality of rock samples and optimizing the model algorithm.…”
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A Short-Term Load Forecasting Method Considering Multiple Factors Based on VAR and CEEMDAN-CNN-BILSTM by Bao Wang, Li Wang, Yanru Ma, Dengshan Hou, Wenwu Sun, Shenghu Li

“…Finally, the sine–cosine and Cauchy mutation sparrow search algorithm (SCSSA) is used to optimize the parameters of the combinative model to improve the forecasting accuracy. …”
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Application of microscopic smartphone attachment for remote preoperative lab testing by Kefan Song, Kefan Song, Alexander T. Adams, Alexander T. Adams

“…The system’s cell counting algorithm was tailored to process the captured images, featuring a streamlined workflow that includes image processing and automated cell detection using a Hough circle algorithm.ResultsThe new system provided good-quality raw images with 26.3 px/μm pixel resolution and 2.19 μm spatial resolution, facilitating effective cell recognition and counting. …”
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A New Hybrid Reinforcement Learning with Artificial Potential Field Method for UAV Target Search by Fang Jin, Zhihao Ye, Mengxue Li, Han Xiao, Weiliang Zeng, Long Wen

“…Specifically, the SAC-APF hybrid framework achieves a 161% improvement in success rate compared to the baseline SAC model, increasing from 0.282 to 0.736 in obstacle scenarios.…”
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Artificial intelligence in electroencephalography analysis for epilepsy diagnosis and management by Chenxi Wang, Chenxi Wang, Xinyue Yuan, Wei Jing

“…Crucially, AI outputs require clinician verification alongside multidimensional clinical data.DiscussionFuture research must prioritize algorithm optimization, data quality improvement, and enhanced AI transparency. …”
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Downscaling of ERA5 reanalysis land surface temperature based on attention mechanism and Google Earth Engine by Shiyu Li, Hong Wan, Qun Yu, Xinyuan Wang

“…In addition, the Bayesian global optimization algorithm is used to select the optimal hyperparameters of the network in order to enhance the predictive performance of the model. …”
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MRI-based habitat analysis of vascular and nerve invasion in the tumor microenvironment: an advanced approach for prostate cancer diagnosis by Bo Guan, Cong Huang, Yalei Wang, Jialong Zhang, Xiaowei Li, Zongyao Hao, Zongyao Hao

“…This approach provides a valuable tool for optimizing treatment plans, improving patient prognosis, and reducing unnecessary medical interventions.…”
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Multi-core helper thread prefetching for irregular data intensive applications by Jian-xun ZHANG, Zhi-min GU, Xiao-han HU, Min CAI

“…Betweenness centrality algorithm was used as a case study, the multi-parameter prefetching model of helper thread and optimized instances were presented and evaluated on commercial CMP platforms Q6600 and I7, the average speedup of betweenness centrality algorithm at different input scale is 1.20 and 1.11 respectively. …”
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Intelligent and automatic irrigation system based on internet of things using fuzzy control technology by Xinying Liu, Zhihuan Zhao, Amin Rezaeipanah

“…This paper presents an automatic, low-cost intelligent irrigation system based on a fuzzy rule-based inference approach and an energy-aware routing algorithm. …”
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Differential Evolution with Autonomous Selection of Mutation Strategies and Control Parameters and Its Application by Zhenyu Wang, Zijian Cao, Zhiqiang Du, Haowen Jia, Binhui Han, Feng Tian, Fuxi Liu

“…Compared with some state-of-the-art intelligence algorithms on 15 functions of CEC2015, 28 functions of CEC2017, and parameter extraction problems of the photovoltaic model, ASDE has the advantages of solution accuracy, convergence speed, and robustness in solving black box problems with unknown features.…”
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Design a low-noise 5GHz wideband microwave power amplifier using 90nm CMOS technology with area reduction employing an active inductor by Taha Naufal Fadhil, A.Z. Yonis

“…We got these results: With an NF of 1.308 and an ideal power usage of 2.64 mW, we achieved an optimal gain of 27.21 dB(S2, 1). Our results show that evolutionary algorithms greatly improve LNA performance by pointing to the best settings for maximizing gain and reducing noise. …”
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A Statistical Analysis Based Probabilistic Routing for Resource-Constrained Delay Tolerant Networks by Jixing Xu, Jianbo Li, Shan Jiang, Chenqu Dai, Lei You

“…In this paper, we propose an improved probabilistic routing algorithm that fully takes into account message's time-to-live when predicting the delivery probability. …”
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