Study on Thermal Conductivity Prediction of Granites Using Data Augmentation and Machine Learning by Yongjie Ma, Lin Tian, Fuhang Hu, Jingyong Wang, Echuan Yan, Yanjun Zhang

“…Results showed that data augmentation significantly improved model performance: the RF model exhibited the best improvement, with its coefficient of determination R² increasing from 0.7489 to 0.9765, Root Mean Square Error (RMSE) decreasing from 0.1870 to 0.1271, and Mean Absolute Error (MAE) reducing from 0.1453 to 0.0993. …”
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IoT-Enhanced Smart Parking Management With IncepDenseMobileNet for Improved Classification by Xiaoxia Zheng, Wenxi Feng, Ning Wang, Huhemandula

“…The IncepDenseMobileNet (IDMN) model integrates Inception, DenseNet, and MobileNet architectures to proficiently capture complex patterns via multi-scale feature extraction and efficient depthwise separable convolutions. …”
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Multiscale Residual Weighted Classification Network for Human Activity Recognition in Microwave Radar by Yukun Gao, Lin Cao, Zongmin Zhao, Dongfeng Wang, Chong Fu, Yanan Guo

“…Moreover, the time–channel weighting mechanism can allocate weights to important time and channel dimensions to achieve more effective extraction of feature information. The model parameters obtained from pre-training are frozen, and the classifier is added to the backend. …”
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Activity Classification for Interactive Game Interfaces by John Darby, Baihua Li, Nick Costen

“…We extract a small number of joint angles at each frame to form a feature vector. Continuous hidden Markov models are then trained with the resulting time series, one for each of a variety of human activity, using the Baum-Welch algorithm. …”
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Deep learning informed multimodal fusion of radiology and pathology to predict outcomes in HPV-associated oropharyngeal squamous cell carcinomaResearch in context by Bolin Song, Amaury Leroy, Kailin Yang, Tanmoy Dam, Xiangxue Wang, Himanshu Maurya, Tilak Pathak, Jonathan Lee, Sarah Stock, Xiao T. Li, Pingfu Fu, Cheng Lu, Paula Toro, Deborah J. Chute, Shlomo Koyfman, Nabil F. Saba, Mihir R. Patel, Anant Madabhushi

“…SMuRF employs cross-modality and cross-region window based multi-head self-attention mechanisms to capture interactions between features across tumour habitats and image scales. Findings: Developed and tested on a cohort of 277 patients with OPSCC with matched radiology and pathology images, SMuRF demonstrated strong performance (C-index = 0.81 for DFS prediction and AUC = 0.75 for tumour grade classification) and emerged as an independent prognostic biomarker for DFS (hazard ratio [HR] = 17, 95% confidence interval [CI], 4.9–58, p < 0.0001) and tumour grade (odds ratio [OR] = 3.7, 95% CI, 1.4–10.5, p = 0.01) controlling for other clinical variables (i.e., T-, N-stage, age, smoking, sex and treatment modalities). …”
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DCFE-YOLO: A novel fabric defect detection method. by Lei Zhou, Bingya Ma, Yanyan Dong, Zhewen Yin, Fan Lu

“…Experimental results demonstrate a significant improvement in detection performance. Specifically, mAP@0.5 increased by 2.9%, precision improved by 3.5%, and mAP@0.5:0.95 rose by 2.3%, highlighting the model's superior capability in detecting complex defects. …”
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Experimental Characterization of Ionic Polymer Metal Composite as a Novel Fractional Order Element by Riccardo Caponetto, Salvatore Graziani, Fulvio L. Pappalardo, Francesca Sapuppo

“…Such analyses take the first steps towards a simplified model of IPMC as a compact electronic FOE for which the fractional exponent value depends on fabrication parameters as the absorption time.…”
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Scene Matching Method for Children’s Psychological Distress Based on Deep Learning Algorithm by Junli Su

“…As a part of machine learning, deep learning can perform mapping transformations in huge data, process huge data with the help of complex models, and extract multilayer features of scene information. …”
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Applying Finite-Element and Boundary-Element Methods to Select Rational Design Version of Weld-Fabricated Bogie Frame Brackets of Locomotives and Railway Cars by O. A. Rusanov, V. V. Kochergin, A. A. Bukhantsev, V. V. Brekson

“…They are selected according to the results of stresses examination in the frame carried out with use of its comprehensive model. Comparative analysis of bracket design versions is performed by varying form, dimensions and location of welded joints and type of edge preparation. …”
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The art of misclassification: too many classes, not enough points by Mario Franco, Gerardo Febres, Nelson Fernández, Carlos Gershenson

“…However, the classification task is ultimately constrained by the intrinsic properties of datasets, independently of computational power or model complexity. In this work, we introduce a formal entropy-based measure of classifiability, which quantifies the inherent difficulty of a classification problem by assessing the uncertainty in class assignments given feature representations. …”
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Texture Analysis of 68Ga-DOTATOC PET/CT Images for the Prediction of Outcome in Patients with Neuroendocrine Tumors by Sara Pellegrino, Mariarosaria Panico, Roberto Bologna, Rocco Morra, Alberto Servetto, Roberto Bianco, Silvana Del Vecchio, Rosa Fonti

“…Survival analysis was performed, including clinical variables along with conventional, volumetric, and texture imaging features. …”
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HG-Mamba: A Hybrid Geometry-Aware Bidirectional Mamba Network for Hyperspectral Image Classification by Xiaofei Yang, Jiafeng Yang, Lin Li, Suihua Xue, Haotian Shi, Haojin Tang, Xiaohui Huang

“…Deep learning has demonstrated significant success in hyperspectral image (HSI) classification by effectively leveraging spatial–spectral feature learning. However, current approaches encounter three challenges: (1) high spectral redundancy and the presence of noisy bands, which impair the extraction of discriminative features; (2) limited spatial receptive fields inherent in convolutional operations; and (3) unidirectional context modeling that inadequately captures bidirectional dependencies in non-causal HSI data. …”
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UE-NER-2025: A GPT-Based Approach to Multi-Lingual Named Entity Recognition on Urdu and English by Muhammad Ahmad, Humaira Farid, Iqra Ameer, Fida Ullah, Muhammad Muzamil, Muhammad Jalal, Ameer Hamza, Ildar Batyrshin, Grigori Sidorov

“…Third, we conducted 30 different experiments using 5-fold cross-validation, combining traditional supervised learning with token-based feature extraction, deep learning with pre-trained word embeddings such as FastText and GloVe, and advanced transfer learning models using contextual embeddings, to evaluate their effectiveness in enhancing NER performance for both English and Urdu, particularly addressing the challenges of low-resource and morphologically rich languages. …”
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In Situ Capture of High-Temperature Precipitate Phases in Ti-48Al-2Cr-2Nb Alloy Using Convolutional Neural Networks by Xiaolei Li, Chuanqing Huang, Sen Zhao, Linlin Cui, Shirui Guo, Bo Zheng, Yinghao Cui, Yongqian Chen, Yue Zhao, Lujun Cui, Chunjie Xu

“…TiAl intermetallic alloy is a crucial high-performance material, and its microstructure evolution at high temperatures is closely related to the process parameters. …”
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Enhancing the Prediction of Dam Deformations: A Novel Data-Driven Approach by Jonas Ziemer, Gideon Stein, Carolin Wicker, Jannik Jänichen, Daniel Klöpper, Katja Last, Joachim Denzler, Christiane Schmullius, Maha Shadaydeh, Clémence Dubois

“…The analysis provided evidence for the following insights: First, the accuracy of current modeling approaches can be greatly improved by utilizing advanced feature engineering and data-driven model selection. …”
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Method for Knowledge Transfer via Multi-Task Semi-Supervised Self-Paced by Yao Zhao, Hongying Liu, Huaxian Pan, Zhen Song, Chunting Liu, Anni Wei, Baoshuang Zhang, Wei Lu

“…With the aid of a self-controlled learning pace, a more robust and globally optimal model can be gradually constructed. Experimental results on several benchmark datasets show that our method achieves a performance gain of 3%-15% in classification accuracy compared to baseline algorithms, along with significant advantages in convergence speed.…”
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Passive Sensing for Mental Health Monitoring Using Machine Learning With Wearables and Smartphones: Scoping Review by ShiYing Shen, Wenhao Qi, Jianwen Zeng, Sixie Li, Xin Liu, Xiaohong Zhu, Chaoqun Dong, Bin Wang, Yankai Shi, Jiani Yao, Bingsheng Wang, Xiajing Lou, Simin Gu, Pan Li, Jinghua Wang, Guowei Jiang, Shihua Cao

“…Data were synthesized across technical dimensions (data collection, preprocessing, feature engineering, and ML models) and clinical associations, with behavioral features categorized into 8 domains. …”
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NFMF: neural fusion matrix factorisation for QoS prediction in service selection by Jianlong Xu, Lijun Xiao, Yuhui Li, Mingwei Huang, Zicong Zhuang, Tien-Hsiung Weng, Wei Liang

“…In this study, we propose a novel QoS prediction model, called neural fusion matrix factorisation, wherein we combine neural networks and matrix factorisation to perform non-linear collaborative filtering for latent feature vectors of users and services. …”
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Leveraging Machine Learning for Pediatric Appendicitis Diagnosis: A Retrospective Study Integrating Clinical, Laboratory, and Imaging Data by Mahdi Navaei, Zohre Doogchi, Fatemeh Gholami, Moein Kermanizadeh Tavakoli

“…Statistical comparisons were performed using independent t‐tests and χ2 tests, with significance set at p < 0.05. …”
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Discrete Wavelet Transform Sampling for Image Super Resolution by Chieh-Li Chen, Heng-Lin Yao, Bo-Lin Jian

“…We evaluate our model on a super-resolution dataset and compare its performance against other networks, highlighting the importance of minimizing trainable parameters for real-time deployment on resource-constrained drone platforms. …”
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