Uncertainty Quantification and Monte Carlo Simulations to Enhance SDG 11.3.1 Monitoring by J. R. Santillan, J. R. Santillan, M. Dorozynski, C. Heipke

“…While this metric is valuable for guiding urban planning, it could be affected by variability in Earth Observation (EO) data products, especially differences in built-up area definitions and classification errors that lead to data quality issues. …”
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Multimodal Fusion Multi-Task Learning Network Based on Federated Averaging for SDB Severity Diagnosis by Songlu Lin, Renzheng Tang, Yuzhe Wang, Zhihong Wang

“…However, real-world polysomnography (PSG) data often suffer from modality heterogeneity, label scarcity, and non-independent and identically distributed (non-IID) characteristics across institutions, posing significant challenges for model generalization and clinical deployment. To address these issues, we propose a federated multi-task learning (FMTL) framework that simultaneously performs sleep staging and SDB severity classification from seven multimodal physiological signals, including EEG, ECG, respiration, etc. …”
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Physiological signal-based mental stress detection using hybrid deep learning models by Nandini Modi, Yogesh Kumar, Kapil Mehta, Neelam Chaplot

“…These spectrograms are then processed by CNN layers to extract spatial patterns, followed by MLP layers for mental stress state classification. Experimental results demonstrates that the hybrid CNN-MLP model achieves superior classification accuracy of 99.45% for training and 99.99% for validation outperforming other conventional models such as Long Short-Term Memory (LSTM), Recurrent Neural Network (RNN) and Transformers. …”
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A Systematic Review of Using Deep Learning Technology in the Steady-State Visually Evoked Potential-Based Brain-Computer Interface Applications: Current Trends and Future Trust Met... by A. S. Albahri, Z. T. Al-qaysi, Laith Alzubaidi, Alhamzah Alnoor, O. S. Albahri, A. H. Alamoodi, Anizah Abu Bakar

“…The literature’s findings in terms of the main aspects identified in existing applications of deep learning pattern recognition techniques in SSVEP-based BCI, such as feature extraction, classification, activation functions, validation methods, and achieved classification accuracies, are examined. …”
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Minimizing Model Size of CNN-Based Vehicle Make Recognition for Frontal Vehicle Images by Wiput Puisamlee, Rathachai Chawuthai

“…Issues included larger models and longer processing times. …”
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Evaluating Binary Classifiers for Cardiovascular Disease Prediction: Enhancing Early Diagnostic Capabilities by Paul Iacobescu, Virginia Marina, Catalin Anghel, Aurelian-Dumitrache Anghele

“…It demonstrates how addressing these issues enhances the reliability and applicability of predictive models. …”
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The Enigmatic Odontogenic Keratocyst: A Cross-sectional Study of Odontogenic Cysts and Tumors Using Ki-67 by Pretty Prince Panakkal, Pramod Philip Matthews, Soma Susan, Nivia Mahadoon, Pinky Varghese, Afshan Pallikkalakathu

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Systematic scoping review of external validation studies of AI pathology models for lung cancer diagnosis by Soumya Arun, Mariia Grosheva, Mark Kosenko, Jan Lukas Robertus, Oleg Blyuss, Rhian Gabe, Daniel Munblit, Judith Offman

“…Models performed various tasks, including classification of malignant versus non-malignant tissue, tumour growth pattern classification and subtyping of adeno- versus squamous cell carcinomas. …”
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YOLOv10-CBRC: A high-precision document image layout analysis model by Zhenjie Wu, Weilan Wang, Hongrui Li

“…However, two significant issues persist: (1) the inadequacy of Tibetan document datasets;(2) insufficient utilization of layout information by existing models. …”
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Heterogeneous appetite patterns in depression: computational modeling of nutritional interoception, reward processing, and decision-making by Yuuki Uchida, Yuuki Uchida, Takatoshi Hikida, Manabu Honda, Yuichi Yamashita

“…Overall, this study findings emphasize the importance of integrating interoception and homeostasis into decision-making frameworks to enhance subtype classification and facilitate the development of effective therapeutic strategies.…”
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Scalable Semantic Adaptive Communication for Task Requirements in WSNs by Hong Yang, Xiaoqing Zhu, Jia Yang, Ji Li, Linbo Qing, Xiaohai He, Pingyu Wang

“…The experimental results show that the proposed SSAC is more robust than traditional and other semantic communication algorithms in image classification tasks, and achieves scalable compression rates without sacrificing classification performance, while improving the bandwidth utilization of the communication system.…”
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Securing the economic management and service infrastructure of banks via the use of artificial intelligence (MO-ILSTM) by Xintong Wu

“…The ILSTM approach is extended from binary data classification to various classifications, addressing the inability of economic management or service infrastructure to efficiently handle complex data processing needs and ensure prompt decision-making. …”
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Midshaft clavicle fractures with associated ipsilateral acromioclavicular joint injuries: a systematic review by Chaoqun Wang, Xugui Li, Shengnan Dong, Wei Xie, Zexi Ling, Chengfei Meng, Ulrich Stöckle

“…Moreover, there is no consensus on the injury mechanism, classification, and treatment, and the prognosis remains poorly summarized. …”
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Leveraging pre-trained models within a semi-supervised and explainable AI RealTime framework: A pioneering paradigm for betel leaf disease detection by Md Tahsin, Maksura Binte Rabbani Nuha, Sumaiya Akter, Al Hossain, Mohammad Rifat Ahmmad Rashid, Raiha Ul Islam, Mohammad Shahadat Hossain

“…The incorporation of semi-supervised and XAI with the best pre-trained model insinuates that the classification process of the healthy leaves from diseased ones can have meticulous consequences without over-fitting issues with less amount of labeled data.…”
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DIA-TSK: A Dynamic Incremental Adaptive Takagi–Sugeno–Kang Fuzzy Classifier by Hao Chen, Chenhui Sha, Mingqing Jiao, Changbin Shao, Shang Gao, Hualong Yu, Bin Qin

“…Extensive comparative experiments demonstrate that the DIA-TSK approaches significantly outperform existing incremental learning methods across multiple dynamic data sets, exhibiting notable advantages in terms of computational efficiency, classification accuracy and memory management. In the experimental comparison, O-DIA-TSK and B-DIA-TSK reach significant superiority in classification performance with respect to comparative methods, with up to 33.3% and 55.8% reductions in training time, respectively, demonstrating the advantage of DIA-TSK in classification tasks using dynamic data.…”
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Study on well logging reservoir fluid evaluation method based on 2D cloud model: A case study of Kuqa Depression, Tarim Basin by WANG Shuli, WANG Jinguo, ZHANG Chengsen, ZHANG Zhean, Kaysar PARHAT, LIU Longcheng

“…Conventional well logging methods rely on petrophysical models that correlate parameters such as porosity, permeability, and oil and gas saturation with reservoir fluid properties to achieve reservoir classification. However, complex geological conditions often lead to issues such as anomalies, multi-factor coupling, and ambiguous fluid boundaries in well logging data. …”
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Parcel-scale crop planting structure extraction combining time-series of Sentinel-1 and Sentinel-2 data based on a semantic edge-aware multi-task neural network by Zhiguang Tang, Xiangdong Wang, Qin Jiang, Haizhu Pan, Gang Deng, He Chen, Yuanhong You, Sijia Li, Haiyan Hou

“…This study proposes a parcel-scale remote sensing methodology for agricultural cultivation pattern extraction to address the classification inaccuracies observed in previous crop planting structure identification studies. …”
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An overview of water erosion modeling in China: a bibliometric and statistical analysis by Wenli RAO, Qingfeng ZHANG, Fengbao ZHANG, Lifeng YUAN, Zicheng ZHENG, Longshan ZHAO, Xiangyang SONG

“…However, there are several issues in current Chinese soil erosion modeling research. …”
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Specificity of Representation of Public Relations in Magazine “Koster” (1970—1980) by N. Yu. Vidineeva

“…The material of the study was the issues of the magazine “Koster” for 1970—1980 in the amount of 6 copies of 52 to 68 pages each. …”
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Educational Data Mining: A Foundational Overview by Ilias Papadogiannis, Manolis Wallace, Georgia Karountzou

“…Some key methodologies addressed include classification, regression analysis, clustering techniques, association rule mining, and Natural Language Processing, among others. …”
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