Automated Body Condition Scoring in Dairy Cows Using 2D Imaging and Deep Learning by Reagan Lewis, Teun Kostermans, Jan Wilhelm Brovold, Talha Laique, Marko Ocepek

“…The study recommends improvements in algorithmic feature extraction, dataset expansion, and multi-view integration to enhance accuracy. …”
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Individual Travel Knowledge Graph-Based Public Transport Commuter Identification: A Mixed Data Learning Approach by Song Hu, Jiancheng Weng, Quan Liang, Wei Zhou, Peizhao Wang

“…The optimal model structure of neuron node number, transfer function, and learning rate are discussed quantitatively according to the minimization of model errors. …”
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Enhancing deep learning methods for brain metastasis detection through cross-technique annotations on SPACE MRI by Tassilo Wald, Benjamin Hamm, Julius C. Holzschuh, Rami El Shafie, Andreas Kudak, Balint Kovacs, Irada Pflüger, Bastian von Nettelbladt, Constantin Ulrich, Michael Anton Baumgartner, Philipp Vollmuth, Jürgen Debus, Klaus H. Maier-Hein, Thomas Welzel

“…Leveraging cross-technique ground truth annotations during training improved the accuracy of DL models in detecting and segmenting BMs. …”
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Measurement Error Estimation Method of Field Service Electricity Energy Meters under the Condition of Big Data by DAI Yan-jie, DONG Xian-guang, LIU Ya-qi, LIANG Ya-jie, LIU Xiao, QI Jia, SUN Yong-quan

“…Firstly, the K-Means clustering algorithm is improved by optimizing the clustering evaluation index, and the field environmental data is analyzed by clustering. …”
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The artificial intelligence revolution in gastric cancer management: clinical applications by Runze Li, Jingfan Li, Yuman Wang, Xiaoyu Liu, Weichao Xu, Runxue Sun, Binqing Xue, Xinqian Zhang, Yikun Ai, Yanru Du, Jianming Jiang

“…The integration of artificial intelligence, from optimizing the diagnosis process and enhancing diagnostic efficiency to promoting the practice of precision medicine, demonstrates its promising prospects for reshaping the treatment model of gastric cancer. …”
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OR-FCOS: an enhanced fully convolutional one-stage approach for growth stage identification of Oudemansiella raphanipes by Runze Fang, Huamao Huang, Nuoyan Guo, Haichuan Wei, Shiyi Wang, Haiying Hu, Ming Liu

“…The enhanced algorithm achieved a mean average precision (mAP) of 89.4% ( $$\hbox {mAP}_{50}$$ ) and 78.3% ( $$\hbox {mAP}_{50:95}$$ ), while the number of model parameters was reduced to 9.9 M, the model size was reduced to 40.1 MB, and the number of floating point operations per second (FLOPs) was reduced to 31.2 G. …”
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Uniform Physics Informed Neural Network Framework for Microgrid and Its Application in Voltage Stability Analysis by Renhai Feng, Khan Wajid, Muhammad Faheem, Jiang Wang, Fazal E. Subhan, Muhammad Shoaib Bhutta

“…This paper proposed an improved PINN, named Uniform Physics Informed Neural Network (UPINN), with Proximal Policy Optimization (PPO) based reinforcement learning, for extortion of parameters of these models. …”
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Equilibrium Analysis of Electricity Market with Multi-Agents Considering Uncertainty by Zhonghai Sun, Runyi Pi, Junjie Yang, Chao Yang, Xin Chen

“…A hybrid solution methodology integrating nonlinear complementarity formulations with genetic algorithm-based optimization was developed. Extensive numerical case studies validate the methodological efficacy, demonstrating improvements in solution optimality and computational efficiency compared to conventional approaches.…”
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Revolutionizing Supply Chain Management With AI: A Path to Efficiency and Sustainability by Kassem Danach, Ali El Dirani, Hassan Rkein

“…Through an in-depth analysis of various AI techniques—such as machine learning, predictive analytics, and optimization algorithms—this study offers novel insights into their applicability in solving complex supply chain problems like demand forecasting, inventory management, and logistics optimization. …”
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Intelligent Counterforce Allocation Method Using Multi-Agent Reinforcement Learning for Ground Operations by Kiwoong Park, Sangheun Shim

“…The MARL agent is implemented using the Soft Actor-Critic algorithm and scaled through a Graph Attention Network to manage over 40 heterogeneous agents (infantry, tanks, and artillery). …”
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Incidence, Pathogenesis, Risk Factors, and Treatment of Cystoid Macula Oedema Following Cataract Surgery: A Systematic Review by Lorenzo Ferro Desideri, Kirupakaran Arun, Enrico Bernardi, Nicola Sagurski, Rodrigo Anguita

“…Further research is needed to establish optimal treatment algorithms and improve outcomes for patients with post-operative CMO…”
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MAB-RSP: Data pricing based on Stackelberg game in MCS by Yongjiao Sun, Xueyan Ma, Anrui Han

“…We introduce two novel contributions. First, the MAB-RS algorithm leverages multi-armed bandit reinforcement learning and a data freshness model to dynamically optimize seller recruitment, efficiently balancing exploration of unknown sellers and exploitation of high-quality ones. …”
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Assessing Urban Vulnerability to Emergencies: A Spatiotemporal Approach Using K-Means Clustering by Ibrahim Mutambik

“…The findings highlight the value of incorporating both spatial and temporal data to enhance emergency response strategies and optimize urban planning efforts. This study contributes to the literature on smart cities by providing a scalable and adaptable method for improving urban resilience in the face of evolving challenges.…”
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Adaptive Feature Selection of Unbalanced Data for Skiing Teaching by Tao Feng

“…Finally, the experimental results on the public imbalanced dataset show that the proposed algorithm effectively improves the classification performance of imbalanced data.…”
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A Novel Framework for Enhancing Decision-Making in Autonomous Cyber Defense Through Graph Embedding by Zhen Wang, Yongjie Wang, Xinli Xiong, Qiankun Ren, Jun Huang

“…Therefore, this paper proposes an enhanced decision-making method combining graph embedding with reinforcement learning algorithms. By constructing a game model for cyber confrontations, this paper models important elements of the network topology for decision-making, which guide the defender to dynamically optimize its strategy based on topology awareness. …”
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Anti-packet-loss joint encoding for voice-over-IP steganography by Zhan-zhan GAO, Guang-ming TANG, Wei-wei ZHANG

“…Furthermore, the influences of key parameters on the performance of joint coding were studied. The selection algorithm for optimal parameters was also given. Experimental results show that the proposed joint coding can effectively improve steganographic resistance to packet loss, and decrease the number of modifications to the voice stream.…”
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Machine learning-based prediction of carotid intima–media thickness progression: a three-year prospective cohort study by An Zhou, Kui Chen, Kui Chen, Yonghui Wei, Qu Ye, Qu Ye, Yuanming Xiao, Rong Shi, Jiangang Wang, Wei-Dong Li

“…Baseline CIMT, absolute monocyte count, sex, age, and LDL-C were identified as the most influential predictors. After Platt scaling, the calibration improved significantly across all the models. …”
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Surface Soil Organic Carbon Estimation Based on Habitat Patches in Southwest China by Jieyun Xiao, Wei Zhou, Ting Wang, Yao Peng, Zhan Shi, Saibo Li, Yang Li, Tianxiang Yue

“…Using the RF feature selection (FS) method to select optimal factors as model input variables improved simulation accuracy compared with using all factors or selecting based on Pearson correlation analysis (R² increased by 1.75%–64.71%). …”
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Enhanced prediction of ventilator-associated pneumonia in patients with traumatic brain injury using advanced machine learning techniques by Negin Ashrafi, Armin Abdollahi, Kamiar Alaei, Maryam Pishgar

“…XGBoost emerged as the top performing algorithm, achieving an AUC of 0.94 and an accuracy of 0.875 on the test set, marking substantial improvements over previously reported best results. …”
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EPIGENETIC REGULATION OF GENE EXPRESSION IN HEAD AND NECK SQUAMOUS CELL CARCINOMA: THERAPEUTIC PERSPECTIVES by R. B. Samsonov, Z. A. Radzhabova, Yu. V. Cheburkin, V. A. Klyuge, E. V. Tkachenko, A. V. Malek

“…Despite the fact that tumors of head and neck are generally available for visual inspection, about 60–70 % of the patients are diagnosed with it at advanced (III or IV) stages of the disease. Unfortunately, optimization of diagnostic algorithms and wide implementation of instrumental diagnostics (ultrasound examination, computed tomography, fiber endoscopy) do not improve the situation. …”
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