Small-Sample Authenticity Identification and Variety Classification of <i>Anoectochilus roxburghii</i> (Wall.) Lindl. Using Hyperspectral Imaging and Machine Learning by Yiqing Xu, Haoyuan Ding, Tingsong Zhang, Zhangting Wang, Hongzhen Wang, Lu Zhou, Yujia Dai, Ziyuan Liu

“…Hyperspectral data were collected from the front and back leaves of nine species of Goldthread and two counterfeit species (Bloodleaf and Spotted-leaf), followed by classification using a variety of machine learning models, including Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Random Forest (RF), Linear Discriminant Analysis (LDA), and Convolutional Neural Networks (CNN). The experimental results demonstrated that the SVM model achieved 100% classification accuracy for distinguishing Goldthread from its counterfeit species, effectively capturing the spectral differences between the front and back leaves. …”
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Predictive analytics in education- enhancing student achievement through machine learning by Sunawar khan, Tehseen Mazhar, Tariq Shahzad, Muhammad Amir khan, Wajahat Waheed, Ahsen Waheed, Habib Hamam

“…Additionally, key predictive factors such as studied credits, entrance results, and regional differences were identified, offering a comprehensive understanding of student performance. …”
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Learner preferences prediction with mixture embedding of knowledge and behavior graph by Xiaoguang LI, Lei GONG, Xiaoli LI, Xin ZHANG, Ge YU

“…To solve the problems of inaccurate prediction of learner preference and insufficient utilization of structural information in the knowledge recommendation model, for the knowledge structure and learner behavior structure in the learner’s preference prediction model, the model of learner preferences predication with mixture embedding of knowledge and behavior graph was proposed.First, considering using graph convolution network (GCN) to fit structural information, GCN was extended to knowledge graph and behavior graph, the purpose of which was to obtain learners’ overall learning pattern and individual learning pattern.Then, the difference between knowledge structure and behavior structure was used to fit learners’ individual preferences, and recurrent neural network was used to encode and decode learners’ preferences to obtain the distribution of learners’ preference distribution.The experimental results on the real datasets demonstrate that the proposed model has a good effect on predicting learner preferences.…”
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Towards interactive AI-authoring with prototypical few-shot classifiers in histopathology by Petr Kuritcyn, Rosalie Kletzander, Sophia Eisenberg, Thomas Wittenberg, Volker Bruns, Katja Evert, Felix Keil, Paul K. Ziegler, Katrin Bankov, Peter Wild, Markus Eckstein, Arndt Hartmann, Carol I. Geppert, Michaela Benz

“…Therefore, we investigated the influence of prototypes originating from images from different scanners and evaluated their performance also on the multiscanner database. …”
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Diagnostic Performance of Artificial Intelligence–Based Methods for Tuberculosis Detection: Systematic Review by Seng Hansun, Ahmadreza Argha, Ivan Bakhshayeshi, Arya Wicaksana, Hamid Alinejad-Rokny, Greg J Fox, Siaw-Teng Liaw, Branko G Celler, Guy B Marks

“…AI performance across different biomarker types showed mean accuracies of 92.45% (SD 7.83%), 89.03% (SD 8.49%), and 84.21% (SD 0%); mean AUCs of 94.47% (SD 7.32%), 88.45% (SD 8.33%), and 88.61% (SD 5.9%); mean sensitivities of 93.8% (SD 6.27%), 88.41% (SD 10.24%), and 93% (SD 0%); and mean specificities of 94.2% (SD 6.63%), 85.89% (SD 14.66%), and 95% (SD 0%) for radiographic, molecular/biochemical, and physiological types, respectively. …”
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Choroidal neovascularization activity and structure by optical coherence tomography angiography in age related macular degeneration by M. A. Kovalevskaya, O. A. Pererva

“…Group 1A: Df – 1.5871 ± 0.05, CVS – 2.29 ± 0.29, area – 11734 ± 4866; group 1B: Df – 1.6462 ± 0.08, CVS – 1.65 ± 0.18, area – 6797 ± 3818; control: Df – 1.9167 ± 0.06, CVS – 1, area – 0. Significant differences were found for CVS (p = 0.0003). Df correlates with the CNV area (p = 0.7) and is probably an unreliable parameter due to incomplete visualization of active CNV.Conclusions. …”
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An Improved V-Net Model for Thyroid Nodule Segmentation by Büşra Yetginler, İsmail Atacak

“…In practice, manual segmentation methods based on ultrasound images are widely used; however, owing to the limitations arising from the imaging sources and differences based on radiologist opinions, their standalone use may not be sufficient for thyroid nodule segmentation. …”
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Revealing Depression Through Social Media via Adaptive Gated Cross-Modal Fusion Augmented With Insights From Personality Traits by Gede Aditra Pradnyana, Wiwik Anggraeni, Eko Mulyanto Yuniarno, Mauridhi Hery Purnomo

“…However, existing multimodal depression detection approaches often adopt rigid fusion strategies and disregard individual differences in expressive behavior by adopting generalized, one-size-fits-all frameworks. …”
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Hybrid AI and semiconductor approaches for power quality improvement by Ravikumar Chinthaginjala, Asadi Srinivasulu, Anupam Agrawal, Tae Hoon Kim, Sivarama Prasad Tera, Shafiq Ahmad

“…The results showed notable differences in performance, with deep learning models, especially LSTM, proving to be more accurate and dependable in identifying and forecasting power quality issues. …”
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Private Data Incrementalization: Data-Centric Model Development for Clinical Liver Segmentation by Stephanie Batista, Miguel Couceiro, Ricardo Filipe, Paulo Rachinhas, Jorge Isidoro, Inês Domingues

“…However, these models often face challenges in adapting to diverse clinical data sources as differences in dataset volume, resolution, and origin impact generalization and 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

“…China is a major cotton-growing country with numerous cotton varieties, each exhibiting significant differences in yield and fiber quality. However, the current management of cottonseed varieties is disorganized, resulting in severe homogenization and the presence of counterfeit and mislabeled varieties. …”
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Experimental Study on Heat Transfer Performance of FKS-TPMS Heat Sink Designs and Time Series Prediction by Mahsa Hajialibabaei, Mohamad Ziad Saghir

“…Among all configurations, the P6 design demonstrated the best performance, with surface temperature differences ranging from 13.1 to 14.2 °C at 0.019 kg/s and a 54.46% higher heat transfer coefficient compared to the P8 design at the lowest mass flow rate. …”
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Wireless Authentication Method Based on Near-Field Feature Fusion Network by QIU Jiefan, ZHOU Kezhong, ZHU Dongfu, ZHANG Jinhong, CHI Kaikai

“…Experimental results demonstrate that the proposed method outperforms existing identity recognition methods, confirming its effectiveness and robustness in different environments and under various conditions.…”
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Rough-and-Refine Model for Scene Graph Generation by Li Junliang, Lv Shirong, Li Wei

“…Predictions for the different paths of the triplet are independently executed using feedforward neural networks. …”
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Differential gray matter correlates and machine learning prediction of abuse and internalizing psychopathology in adolescent females by Sara A. Heyn, Taylor J. Keding, Josh Cisler, Katie McLaughlin, Ryan J. Herringa

“…First, we characterized how differences in GMV associated with childhood abuse exposure depend on the presence or absence of IP using voxel-based morphometry (VBM). …”
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Development and Evaluation of Machine Learning Models for Air-to-Land Temperature Conversion Using the Newly Established Kunlun Mountain Gradient Observation System by Yongkang Li, Qing He, Yongqiang Liu, Amina Maituerdi, Yang Yan, Jiao Tan

“…The results revealed significant discrepancies between the monthly average LST derived from polar-orbiting satellites and the hourly composite monthly LST measured on-site or under ideal cloud-free conditions. These differences were particularly pronounced in high-altitude regions (4000 m and above), with the greatest differences occurring in winter, reaching up to 10.2 °C. …”
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Learner preferences prediction with mixture embedding of knowledge and behavior graph by Xiaoguang LI, Lei GONG, Xiaoli LI, Xin ZHANG, Ge YU

“…To solve the problems of inaccurate prediction of learner preference and insufficient utilization of structural information in the knowledge recommendation model, for the knowledge structure and learner behavior structure in the learner’s preference prediction model, the model of learner preferences predication with mixture embedding of knowledge and behavior graph was proposed.First, considering using graph convolution network (GCN) to fit structural information, GCN was extended to knowledge graph and behavior graph, the purpose of which was to obtain learners’ overall learning pattern and individual learning pattern.Then, the difference between knowledge structure and behavior structure was used to fit learners’ individual preferences, and recurrent neural network was used to encode and decode learners’ preferences to obtain the distribution of learners’ preference distribution.The experimental results on the real datasets demonstrate that the proposed model has a good effect on predicting learner preferences.…”
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A Multimodal Fatigue Detection System Using sEMG and IMU Signals with a Hybrid CNN-LSTM-Attention Model by Soree Hwang, Nayeon Kwon, Dongwon Lee, Jongman Kim, Sumin Yang, Inchan Youn, Hyuk-June Moon, Joon-Kyung Sung, Sungmin Han

“…These results highlight the system’s robustness for personalized fatigue monitoring, surpassing traditional subject-dependent methods by addressing inter-individual differences.…”
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An artificial intelligence algorithm for the detection of pulmonary ground-glass nodules on spectral detector CT: performance on virtual monochromatic images by Zhong-Yan Ma, Hai-lin Zhang, Fa-jin Lv, Wei Zhao, Dan Han, Li-chang Lei, Qin Song, Wei-wei Jing, Hui Duan, Shao-Lei Kang

“…And Delong’s test indicated that the differences between the area-under-the-curve (AUC) values of the CPIs group and the VMIs group were not statistically significant (P > 0.05). …”
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A study on land use change simulation based on PLUS model and the U-net structure: A case study of Jilin Province by Jiafu Liu, Xiangli Kong, Yue Zhu, Baihao Zhang

“…These results demonstrate that both models possess reliable simulation performance. 2) The two methods exhibit significant differences in predictive performance. The U-Net model, which utilizes convolutional neural networks to extract multi-scale spatial features and addresses the class imbalance issue with the OHEM-Dice composite function, significantly enhances the prediction accuracy of nonlinear dynamics. …”
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