Non-Line-of-Sight Location With Gauss Filtering Algorithm Based on a Model of Photon Flight by Yu Ren, Zongliang Xie, Yihan Luo, Shaoxiong Xu, Haotong Ma, Yi Tan

“…To improve the authenticity of the model, the bidirectional reflectance distribution function (BRDF) is used to characterize the scattering process. …”
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Research on Reservoir History Matching Method Based on Real⁃Coded Genetic Algorithm and Connectivity Model by Ainiwaer AILIYAER, Chunli ZHAO, Feng LIU

“…A adaptive selection strategies, crossover, and mutation operations are introduced in this paper to further enhance the algorithm's performance. Application of RGA to the history matching problem in a mechanistic model demonstrates that RGA can effectively improve fitting results and find relatively optimal solutions in a short time. …”
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The Two-Stage Alzheimer’s Disease Automatic Diagnosis Algorithm Based on ST-MBV3 Model by Guiping Li, Zhenhao Jin, Minghui Deng, Junjie Gong, Piaoyi Zheng

“…This study focuses on leveraging these advancements to enhance the accuracy of AD diagnosis through a novel two-stage algorithm. The proposed model combines an improved 3D DenseNet segmentation model with an enhanced ST-MBV3 classification model. …”
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Enhancing Surgery Scheduling in Health Care Settings With Metaheuristic Optimization Models: Algorithm Validation Study by João Lopes, Tiago Guimarães, Júlio Duarte, Manuel Santos

“…By implementing these AI-driven strategies, hospitals can minimize patient wait times, maximize resource use, and enhance surgical outcomes through improved planning. This development highlights how AI algorithms can effectively adapt to changing health care environments, having a transformative impact.…”
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Optimized SVR with nature-inspired algorithms for environmental modelling of mycotoxins in food virtual-water samples by Abdullahi G. Usman, Sagiru Mati, Hanita Daud, Ahmad Abubakar Suleiman, Sani I. Abba, Hijaz Ahmad, Taha Radwan

“…This study proposed the use of a support vector regression (SVR) predictive model improved by two metaheuristic algorithms used for optimization namely, Harris Hawks Optimization (HHO) and Particle Swarm Optimization (PSO) to predict chromatographic retention time of various food mycotoxin groups. …”
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Automated weed and crop recognition and classification model using deep transfer learning with optimization algorithm by K. Gopalakrishnan, R. Sivaraj, M. Vijayakumar

“…The extensive outcome highlighted the improvements of the developed AWRC-DLMLO technique with other existing models.…”
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An equivalent modeling method for integrated water-wind-solar systems based on sparrow search algorithm by Yuanhong Lu, Jie Zhang, Jingyue Zhang, Libin Huang, Haiping Guo, Binjiang Hu, Tianyu Guo

“…This study proposes a cluster-based equivalent modeling approach for hybrid wind-solar-hydro power plants using the Sparrow Search Algorithm. …”
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Anomaly Detection and Performance Analysis With Exponential Smoothing Model Powered by Genetic Algorithms and Meta Optimization by Ali Kerem Guler, Huseyin Fuat Alsan, Taner Arsan

“…This study employs a genetic algorithm to optimize the parameters of the Third Order Exponential Smoothing model for predicting on the real-time traffic datasets of the Numenta Anomaly Benchmark (NAB). …”
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Multi-target detection of underground personnel based on an improved YOLOv8n model by WEN Yongzhong, JIA Pengtao, XIA Mingao, ZHANG Longgang, WANG Weifeng

“…An innovative multi-target detection algorithm, YOLOv8n-MSMLAS, was proposed based on the YOLOv8n network structure. …”
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Detection Model for Cotton Picker Fire Recognition Based on Lightweight Improved YOLOv11 by Zhai Shi, Fangwei Wu, Changjie Han, Dongdong Song, Yi Wu

“…The improved detection algorithm maintains high accuracy while achieving faster inference speed and fewer model parameters. …”
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Multiradar Collaborative Task Scheduling Algorithm Based on Graph Neural Networks with Model Knowledge Embedding by Haoqing LI, Dian YU, Changchun PAN, Wenxian YU, Dongying LI

“…Therefore, this paper proposes a Model Knowledge Embedded Graph Neural Network (MKEGNN) scheduling algorithm. …”
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Exploring the Capacity of Large Language Models to Assess the Chronic Pain Experience: Algorithm Development and Validation by Jacopo Amidei, Rubén Nieto, Andreas Kaltenbrunner, Jose Gregorio Ferreira De Sá, Mayte Serrat, Klara Albajes

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A combined model of shoot phosphorus uptake based on sparse data and active learning algorithm by Tianli Wang, Yi Zhang, Haiyan Liu, Fei Li, Dayong Guo, Ning Cao, Yubin Zhang

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An Effective Hybrid Metaheuristic Approach Based on the Genetic Algorithm by Olympia Roeva, Dafina Zoteva, Gergana Roeva, Maya Ignatova, Velislava Lyubenova

“…The evolutionary approach (EA) is applied to form the initial population of the GA, thus improving the algorithm’s performance, especially its convergence speed. …”
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A multi-algorithm prognostic model combining inflammatory indices and surgical features in distal cholangiocarcinoma by Yi Yin, Yi Yin, Luyuan Bai, Xinyue Mu, Xinyue Mu, Shan Zhang, Panpan Zhai, Panpan Zhai

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Model-based estimation of tokamak plasma profiles and physics parameters: algorithm overview and application to ITER by S. Van Mulders, F. Felici, S.C. McIntosh, F. Carpanese, C.E. Contré, R. Coosemans, O. Kudláček, F. Pastore, S.D. Pinches, M. Reisner, O. Sauter

“…In this work, a model-based estimation algorithm is proposed to improve real-time and post-shot estimation of tokamak plasma profiles. …”
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Service clustering method based on description context feature words and improved GSDMM model by Qiang HU, Jiaji SHEN, Guanghui JING, Junwei DU

“…To address the problem that current service clustering methods usually faced low quality of service representation vectors, a service clustering method based on description context feature words and improved GSDMM model was proposed.Firstly, a feature word extraction method based on context weight was constructed.The words that fit well with the context of service description were extracted as the set of feature words for each service.Then, an improved GSDMM model with topic distribution probability correction factor was established to generate service representation vectors and achieve distribution probability correction for non-critical topic items.Finally, K-means++ algorithm was employed to cluster Web services based on these service representation vectors.Experiments were conducted on real Web services in Web site of Programmable Web.Experiment results show that the quality of service representation vectors generated by the proposed method is higher than of other topic models.Further, the performance of our clustering method is significantly better than other service clustering methods.…”
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Construction and clinical visualization application of a predictive model for mortality risk in sepsis patients based on an improved machine learning model by Ting Chen, Xuefeng Zhang, Qunfeng Yu, Qin Yang, Lingmin Yuan, Fei Tong

“…A Self-Weighted Self-Evolutionary Learning Model (SWSELM) was developed to identify independent risk factors for sepsis mortality and to create a visualization system for clinical application.ResultsThe improved algorithm significantly outperformed other algorithms on 23 standard test functions. …”
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Prediction of Telkomsel 4G LTE Card Sales using The K-Nearest Neighbor Algorithm by Alfiana Fontes Martins, Yasinta Oktaviana Legu Rema, Debora Chrisinta, Alejandro Jr. V. Matute, Krisantus Jumarto Tey Seran

“…Accurate sales prediction is a critical challenge in business decision-making, as factors such as data imbalance, outliers, and overfitting may compromise the reliability of predictive models. This study aims to develop a precise model for predicting card sales using the K-Nearest Neighbor (KNN) algorithm and to offer recommendations for improving prediction quality by addressing issues related to data imbalance and overfitting. …”
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An Innovative Indoor Localization Method for Agricultural Robots Based on the NLOS Base Station Identification and IBKA-BP Integration by Jingjing Yang, Lihong Wan, Junbing Qian, Zonglun Li, Zhijie Mao, Xueming Zhang, Junjie Lei

“…Next, the collected received signal strength indication (RSSI) data are processed using Kalman filtering and Min-Max normalization, suppressing signal fluctuations and accelerating the gradient descent convergence of the distance measurement model. Finally, the improved black kite algorithm (IBKA) is enhanced with tent chaotic mapping, a lens imaging reverse learning strategy, and the golden sine strategy to optimize the weights and biases of the BP neural network, developing an RSSI-based ranging algorithm using the IBKA-BP neural network. …”
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