Named Entity Recognition in Aviation Products Domain Based on BERT by Mingye Yang, Bernadin Namoano, Maryam Farsi, John Ahmet Erkoyuncu

“…Through experiments on the constructed aviation product dataset, the model achieved a Precision value of 91.74%, a Recall value of 92.46%, and an F1 score of 92.1%, Compared with other baseline models, the F1-score is improved by 0.9% to 1.5%. …”
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YOLO11-ARAF: An Accurate and Lightweight Method for Apple Detection in Real-World Complex Orchard Environments by Yangtian Lin, Yujun Xia, Pengcheng Xia, Zhengyang Liu, Haodi Wang, Chengjin Qin, Liang Gong, Chengliang Liu

“…In response to the insufficient recognition performance and poor generalization capacity of existing detection algorithms under unstructured orchard scenarios, we constructed a customized apple image dataset captured under varying illumination conditions and introduced an improved detection architecture, YOLO11-ARAF, derived from YOLO11. …”
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Deep learning radiomics nomogram predicts lymph node metastasis in laryngeal squamous cell carcinoma by Yun Liang, Yun Liang, Min He, Wenqing Chen, Wenqing Chen, Lizhen Li, Lizhen Li, Yumeng Dong, Yumeng Dong, Gang Liang, Hui Huangfu, Zengyu Jiang, Zengyu Jiang, Sheng He, Sheng He, Sheng He

“…Radiomics features were extracted from CT images, and 7 machine learning algorithms were used to develop 7 radiomics models, which were combined with deep learning features extracted from the ResNet50 deep learning network to form deep learning radiomics (DLR) models. …”
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Empowering Education: Leveraging Clustering and Recommendations for Enhanced Student Insights by Kheira Ouassif, Benameur Ziani, Jorge Herrera-Tapia, Chaker Abdelaziz Kerrache

“…The overarching goal was to refine adaptive learning models that cater to the diverse needs of students, improving their academic success and participation.…”
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Advancing Agricultural Machinery Maintenance: Deep Learning-Enabled Motor Fault Diagnosis by Xusong Bai, Qian Chen, Xiangjin Song, Weihang Hong

“…This article further discusses future research directions, such as optimizing DL models for real-time processing, improving robustness under varying agricultural conditions, and developing user-friendly interfaces for farmers and technicians. …”
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BucketAugment: Reinforced Domain Generalisation in Abdominal CT Segmentation by David Jozef Hresko, Peter Drotar

“…<italic>Methods:</italic> BucketAugment leverages principles from the Q-learning algorithm and employs validation loss to search for an optimal policy within a search space comprised of distributed stacks of 3D volumetric augmentations, termed &#x2018;buckets.…”
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Investigating employment patterns and determinants in the European Union through panel data insights by Vasilescu Maria Denisa, Stănilă Larisa, Crivoi Silvana, Belu Maria Berta

“…The clustering algorithm identified the heterogeneity of the countries, indicating an optimal number of three clusters for the grouping of EU states, considering the set of variables used. …”
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Research on Mobile Agent Path Planning Based on Deep Reinforcement Learning by Shengwei Jin, Xizheng Zhang, Ying Hu, Ruoyuan Liu, Qing Wang, Haihua He, Junyu Liao, Lijing Zeng

“…A sensor-driven three-dimensional simulation environment featuring static obstacles was constructed using the ROS and Gazebo platforms, where LiDAR-equipped mobile agent models were trained for real-time environmental perception and strategy optimization prior to deployment on experimental vehicles. …”
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Research Status and Progress of Acoustic Fire Extinguishing Technology by Xinyue Shi, Zhaojun Tian, Yi Lu, Qing Ye

“…Future advancements require breakthroughs in acoustic metamaterials, the integration of intelligent algorithms, and the collaborative optimization of multi-technology systems to facilitate the transition of acoustic wave-based fire suppression from laboratory settings to real-world industrial firefighting applications. …”
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An in-depth exploration of supervised and semi-supervised learning on face recognition by Purnawansyah, Wibawa Aji Prasetya, Widiyaningtyas Triyanna, Haviluddin, Darwis Herdianti, Azis Huzain

“…Although there are some instances of sub-optimal performance in certain algorithms like CNN on specific datasets, this research provides comprehensive insights into the effectiveness of various models in contexts of limited-label learning. …”
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Tool wear prediction based on XGBoost feature selection combined with PSO-BP network by Zhangwen Lin, Yankun Fan, Jinling Tan, Zhen Li, Peng Yang, Hua Wang, Weiwei Duan

“…Experimental results show that PSO outperforms other algorithms in training the tool wear prediction model, with XGBoost feature selection reducing model construction time by 57.4% and increasing accuracy by 63.57%, demonstrating superior feature selection capabilities over Decision Tree, Random Fores, Adaboost and Extra Trees. …”
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Prediction of Myocardial Infarction Based on Non-ECG Sleep Data Combined With Domain Knowledge by Changyun Li, Yonghan Zhao, Qihui Mo, Zhibing Wang, Xi Xu

“…The experiments demonstrate that the model&#x2019;s accuracy reaches its optimal level by combining the age rule, improving to 73.1%. …”
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Enhanced Position-Aided Beam Prediction Using Real-World Data and Enhanced-Convolutional Neural Networks by Ahmed Abd El Moaty Mohamed Gouda, Ehab K. I. Hamad, Aziza I. Hussein, M. Mourad Mabrook, A. A. Donkol

“…The proposed E-CNN model has been investigated across nine different scenarios from the DeepSense 6G dataset and compared against the conventional algorithms. …”
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Enhanced identification of Morganella spp. using MALDI-TOF mass spectrometry by Mathilde Duque, Cécile Emeraud, Rémy A. Bonnin, Quentin Giai-Gianetto, Laurent Dortet, Alexandre Godmer

“…Methods: We applied Machine Learning (ML) algorithms to a collection of 235 clinicial Morganella spp. strains to develop an optimized identification model. …”
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Advanced Interpretation of Bullet-Affected Chest X-Rays Using Deep Transfer Learning by Shaheer Khan, Nirban Bhowmick, Azib Farooq, Muhammad Zahid, Sultan Shoaib, Saqlain Razzaq, Abdul Razzaq, Yasar Amin

“…Special deep learning algorithms went through a process of optimization before researchers improved their ability to detect and place objects. …”
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POSSIBILITIES OF ARTIFICIAL INTELLIGENCE IN COMPUTER-AIDED DESIGN OF STAINED GLASS ART by Andrii Petrushevskyi, Yevhen Antonovych

“…The scientificance novelty The analysis of artificial intelligence capabilities in the form of automated algorithms for the development and optimization of stained glass design is a new research direction. …”
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The future of critical care: AI-powered mortality prediction for acute variceal gastrointestinal bleeding and acute non-variceal gastrointestinal bleeding patients by Zhou Liu, Guijun Jiang, Liang Zhang, Palpasa Shrestha, Yugang Hu, Yi Zhu, Guang Li, Yuanguo Xiong, Liying Zhan

“…In case of ANGIB patients, gradient boosting model proven to be the optimal machine learning models, with the AUC of 0.985 ± 0.002, accuracy of 0.948 ± 0.009, precision of 0.949 ± 0.009, recall of 0.968 ± 0.009, and F1 score of 0.959 ± 0.007. …”
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VTGAN based proactive VM consolidation in cloud data centers using value and trend approaches by Aya I. Maiyza, Hanan A. Hassan, Walaa M. Sheta, Karim Banawan, Noha O. Korany

“…Additionally, incorporating VTGAN into the VM placement algorithm to disregard hosts predicted to become overloaded further improves performance. …”
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A Rapid Identification Method for Cottonseed Varieties Based on Near-Infrared Spectral and Generative Adversarial Networks by Qingxu Li, Hao Li, Renhao Liu, Xiaofeng Dong, Hongzhou Zhang, Wanhuai Zhou

“…Data augmentation using GAN-CNIRD-generated cottonseed data improved the accuracy of the three optimal models by 6%, 5%, and 6%, respectively. …”
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Robust vector-weighted and matrix-weighted multi-view hard c-means clustering by Zhe Liu, Sarah Aljohani, Sijia Zhu, Tapan Senapati, Gözde Ulutagay, Salma Haque, Nabil Mlaiki

“…On this basis, we further propose a robust matrix-weighted multi-view hard c-means (MW-MVHCM) clustering, which assigns view-specific weights at the cluster level, allowing for more detailed intra-view contribution modeling. This matrix-weighted approach enables MW-MVHCM to dynamically capture the varying importance of each view across clusters, improving clustering performance. …”
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