Improved stereo matching network based on dense multi-scale feature guided cost aggregation by ZHANG Bo, ZHANG Meiling, LI Xue, ZHU Lei

“…To further improve the disparity prediction accuracy of stereo matching algorithm in the ill-posed regions such as repeating textures, no texture, and edge, an improved dense multi-scale feature guided aggregation network (DGNet) based on PSMNet was proposed. …”
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ADMET evaluation in drug discovery: 21. Application and industrial validation of machine learning algorithms for Caco-2 permeability prediction by Dong Wang, Jieyu Jin, Guqin Shi, Jingxiao Bao, Zheng Wang, Shimeng Li, Peichen Pan, Dan Li, Yu Kang, Tingjun Hou

“…We believe that the model developed in this study could represent a reliable tool for assessing Caco-2 permeability during early-stage drug discovery and the chemical transformation rules derived here could provide insights for optimizing Caco-2 permeability. Scientific contribution A comprehensive validation of various machine learning algorithms combined with diverse molecular representations on a large dataset for predicting Caco-2 permeability was reported. …”
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Capacity optimization strategy for gravity energy storage stations considering the impact of new power systems. by Can Lv, Jun He, Jingjing Ma, Yukun Yang, Fan Liu, Wentao Huang

“…The model is solved using an enhanced Grasshopper Optimization Algorithm (W-GOA) incorporating a whale spiral motion strategy to improve convergence and solution accuracy. …”
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Damage Identification in Large-Scale Structures Using Time Series Analysis and Improved Sparse Regularization by Huihui Chen, Xiaojing Yuan

“…Compared to the moth-flame optimization (MFO) algorithm and traditional regularization methods, for both noise-free and noise polluted data, the iteration curves illustrate that the proposed method can achieve convergence within about 200 iterations, while the MFO algorithm is always trapped into local optima; meanwhile, the traditional regularization method needs more iterations or even cannot meet the preset tolerance. …”
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Impact of monitor unit optimization in volumetric modulated arc therapy planning for nasopharyngeal carcinoma. by Huaqu Zeng, Zhen Li, Zongyou Chen, Shukui Tang, Qifu Lin, Minzhi Zhong, Zunbei Wen

“…Dual-arc VMAT plan were designed using photon optimization algorithms without the monitor unit objective (MUO) tool, denoted as the base plan. …”
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Structured Summarization of League of Legends Match Data Optimized for Large Language Model Input by Jooyoung Kim, Wonkyung Lee, Jungwoon Park

“…This paper introduces the League of Legends Match Data Compactor (LoL-MDC), a tool designed to transform extensive match data into a concise and structured format optimized for LLM processing. By systematically summarizing structured match information—including match overviews, player and team statistics, timeline summaries, and algorithmically selected key events—the LoL-MDC significantly reduces the data size from approximately 80,000 tokens to under 2000 tokens while retaining analytical value. …”
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URT-YOLOv11: A Large Receptive Field Algorithm for Detecting Tomato Ripening Under Different Field Conditions by Di Mu, Yuping Guou, Wei Wang, Ran Peng, Chunjie Guo, Francesco Marinello, Yingjie Xie, Qiang Huang

“…This study proposes an improved YOLOv11 model to address the limitations of traditional tomato recognition algorithms in complex agricultural environments, such as lighting changes, occlusion, scale variations, and complex backgrounds. …”
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Lightweight Anomaly-Based Detection Using Cuckoo Search Algorithm and Decision Tree to Mitigate Man-in-the-Middle Attacks in DNS by Ramahlapane Lerato Moila, Mthulisi Velempini

“…By integrating the Cuckoo Search Algorithm, the scheme minimizes false positives while improving the detection rate. …”
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A Comparative Analysis of Hyper-Parameter Optimization Methods for Predicting Heart Failure Outcomes by Qisthi Alhazmi Hidayaturrohman, Eisuke Hanada

“…This study reveals that while model selection is crucial, an appropriate optimization method and imputation technique significantly impact model performance. …”
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Multi-Objective Optimization of Machinability and Energy Consumption of Cast Iron Depending on Cooling Rate by Burak Öztürk, Fuat Kara

“…Using genetic algorithms (GA) and Response Surface Methodology (RSM), this study optimized machining parameters for energy consumption and surface finish. …”
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Design and Experimental Validation of Wireless Electric Vehicle Charger Control Using Genetic Algorithms and Feedforward Artificial Neural Network by Marouane El Ancary, Abdellah Lassioui, Hassan El Fadil, Yassine El Asri, Anwar Hasni, Soukaina Nady

“…The ANN estimates the system’s efficiency, ensuring optimal performance during the charging process. The developed control strategy significantly improved energy transfer efficiency and system stability. …”
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Enhancing UAV Security Against GPS Spoofing Attacks Through a Genetic Algorithm-Driven Deep Learning Framework by Abdallah Al-Sabbagh, Aya El-Bokhary, Sana El-Koussa, Abdulrahman Jaber, Mahmoud Elkhodr

“…This paper presents an enhanced integration of Long Short-Term Memory (LSTM) networks with a Genetic Algorithm (GA) for GPS spoofing detection. Although GA–neural network combinations have existed for decades, our method expands the GA’s search space to optimize a wider range of hyperparameters, thereby improving adaptability in dynamic operational scenarios. …”
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A New Approach Combining RSA and ElGamal Algorithms: Advancements in Encryption and Digital Signatures Using Gaussian Integers by Yahia Awad, Douaa Jomaa, Yousuf Alkhezi, Ramiz Hindi

“…Quantitative assessments are provided to evaluate the effectiveness and computational efficiency of the proposed system. While key generation is slightly slower compared to using RSA or ElGamal individually, our approach delivers comparable performance in encryption and decryption, with notable improvements in robustness and versatility. …”
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A Near-Real-Time Imaging Algorithm for Focusing Spaceborne SAR Data in Multiple Modes Based on an Embedded GPU by Yunju Zhang, Mingyang Shang, Yini Lv, Xiaolan Qiu

“…Subsequently, through a series of optimizations of the algorithm, the efficiency of the algorithm was further improved. …”
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ML-Based Self-Optimization Handover Technique for Beyond 5G Mobile Network by Saddam Alraih, Rosdiadee Nordin, Asma Abu-Samah, Ibraheem Shayea, Nor Fadzilah Abdullah

“…The results demonstrate that ML-SOHOT enhanced the HO optimization performance significantly and surpassed the competitive algorithms. …”
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The Study of Roadside Visual Perception in Internet of Vehicles Based on Improved YOLOv5 and CombineSORT by LI Xiaohui, YANG Jie, XIA Qin

“…On the contrary, the algorithms applying YOLOv5, YOLOX, YOLOv7 and the paper's improved YOLOv5 achieved the recall rates from 95.26% to 96.28%, while algorithms applying DeepSORT, StrongSORT, Bot-SORT and CombineSORT achieved the MOTA values from 0.887 to 0.901. …”
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Predicting COVID-19 severity in pediatric patients using machine learning: a comparative analysis of algorithms and ensemble methods by Babak Pourakbari, Setareh Mamishi, Sepideh Keshavarz Valian, Shima Mahmoudi, Reihaneh Hosseinpour Sadeghi, Mohammad Reza Abdolsalehi, Mahmoud Khodabandeh, Mohammad Farahmand

“…This study evaluates the performance of machine learning algorithms in predicting disease severity among pediatrics. …”
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An adaptive k-means clustering algorithm based on grid and domain centroid weights for digital twins in the context of digital transformation by Wei Cai, Fei Yang, Bo Yao, Chuanxian Li, Guangyu Sun

“…For newly arriving data, it employs an incremental update mechanism, adjusting only the affected portions, which significantly reduces memory usage. While maintaining clustering accuracy comparable to k-means, the GDCW-AKM algorithm greatly improves overall efficiency, with the performance gain becoming more pronounced as data volume increases. …”
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Review of Demand Response-based Optimal Scheduling of Electric and Thermal Integrated Energy Systems by Jingshan MO, Guangxian YAN, Na SONG, Mingyang YUAN

“…Artificial intelligence algorithms are primarily divided into methods based on group optimization problems and machine learning algorithms. …”
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Dynamic Error Modeling and Predictive Compensation for Direct-Drive Turntables Based on CEEMDAN-TPE-LightGBM-APC Algorithm by Manzhi Yang, Hao Ren, Shijia Liu, Bin Feng, Juan Wei, Hongyu Ge, Bin Zhang

“…Our methodology comprises four key stages: Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN)-based decomposition of historical error data, development of component-specific prediction models using Tree-structured Parzen Estimator (TPE)-optimized Light Gradient Boosting Machine (LightGBM) algorithms for each Intrinsic Mode Function (IMF), integration of component predictions to generate initial values, and application of the Adaptive Prediction Correction (APC) module to produce final predictions. …”
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