Learning models for predicting pavement friction based on non-contact texture measurements: Comparative assessment by Xiuquan Lin, You Zhan, Zilong Nie, Joshua Qiang Li, Xinyu Zhu, Allen A. Zhang

“…By assessing the importance of the 38 parameter variables, the most critical 21 variables were selected for model development. …”
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Advancing arabic dialect detection with hybrid stacked transformer models by Hager Saleh, Hager Saleh, Hager Saleh, Abdulaziz AlMohimeed, Rasha Hassan, Mandour M. Ibrahim, Saeed Hamood Alsamhi, Moatamad Refaat Hassan, Sherif Mostafa

“…The improvement in classification performance highlights the wider variety of linguistic variables that the model can capture, providing a reliable solution for precise Arabic dialect recognition and improving the efficacy of NLP applications. …”
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Modeling Temperature in the Ecuadorian Paramo Through Deep Learning by Marco Javier Castelo Cabay, Jose Antonio Piedra-Fernandez, Rosa Maria Ayala

“…The prediction integrates key variables such as humidity, precipitation, and wind speed through multivariate neural networks. …”
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Scalable recurrence graph network for stratifying RhoB texture dynamics in rectal cancer biopsies by Tuan D. Pham

“…RhoB, a key biomarker assessed via immunohistochemistry, is crucial in predicting responses to radiotherapy (RT), but variability in staining techniques and tumor heterogeneity often complicate these assessments. …”
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From pixels to planning: scale-free active inference by Karl Friston, Karl Friston, Conor Heins, Tim Verbelen, Lancelot Da Costa, Lancelot Da Costa, Tommaso Salvatori, Dimitrije Markovic, Dimitrije Markovic, Alexander Tschantz, Magnus Koudahl, Christopher Buckley, Christopher Buckley, Thomas Parr

“…This model generalizes partially observed Markov decision processes to include paths as latent variables, rendering it suitable for active inference and learning in a dynamic setting. …”
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Image-Based Breast Cancer Histopathology Classification and Diagnosis Using Deep Learning Approaches by Lama A. Aldakhil, Haifa F. Alhasson, Shuaa S. Alharbi, Rehan Ullah Khan, Ali Mustafa Qamar

“…We believe that by examining several factors and variables and conducting an in-depth analysis of the state of the art, this study will contribute to the state of the art and benefit researchers in both computing and medical domains.…”
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Keypoints-Based Multi-Cue Feature Fusion Network (MF-Net) for Action Recognition of ADHD Children in TOVA Assessment by Wanyu Tang, Chao Shi, Yuanyuan Li, Zhonglan Tang, Gang Yang, Jing Zhang, Ling He

“…This work proposes a novel keypoints-based system, the Multi-cue Feature Fusion Network (MF-Net), for recognizing actions and behaviors of children with ADHD during the Test of Variables of Attention (TOVA). The system aims to assess ADHD symptoms as described in the DSM-V by extracting features from human body and facial keypoints. …”
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Structural Similarity-Guided Siamese U-Net Model for Detecting Changes in Snow Water Equivalent by Karim Malik, Colin Robertson

“…We conclude with a discussion on the implications of the findings from our study of snow dynamics and climate variables using gridded SWE data, computer vision metrics, and fully convolutional deep neural networks.…”
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Modeling and Evaluating the Impact of Mobile Usage on Pedestrian Behavior at Signalized Intersections: A Machine Learning Perspective by Faizanul Haque, Farhan Ahmad Kidwai, Ishwor Thapa, Sufyan Ghani, Lincoln M. Mtapure

“…Key inputs to the modeling process include pedestrian demographics (age, gender, group size) and behavioral variables (crossing speed, waiting time, compliance behaviors). …”
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Method of tail beam posture prediction of top coal caving hydraulic support based on LSTM by Yupeng YAO, Jinglin ZHANG, Wu XIONG

“…The absolute coordinates of the support bottom plate, the inclination of the tail beam, the relative height of the tail beam, the frame shifting rate and the column pressure related to the tail beam caving action were used as the input variables of the RNN convolutional network and the LSTM neural network. …”
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Unified Deep Learning Model for Global Prediction of Aboveground Biomass, Canopy Height, and Cover from High-Resolution, Multi-Sensor Satellite Imagery by Manuel Weber, Carly Beneke, Clyde Wheeler

“…We further show that our pre-trained model facilitates seamless transferability to other GEDI variables due to its multi-head architecture.…”
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FTIR-Based Microplastic Classification: A Comprehensive Study on Normalization and ML Techniques by Octavio Villegas-Camacho, Iván Francisco-Valencia, Roberto Alejo-Eleuterio, Everardo Efrén Granda-Gutiérrez, Sonia Martínez-Gallegos, Daniel Villanueva-Vásquez

“…The findings highlight the effectiveness of FTIR spectra with broad and variable ranges for the automated classification of microplastics using ML techniques, along with appropriate normalization methods.…”
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Deep Learning and Image Generator Health Tabular Data (IGHT) for Predicting Overall Survival in Patients With Colorectal Cancer: Retrospective Study by Seo Hyun Oh, Youngho Lee, Jeong-Heum Baek, Woongsang Sunwoo

“…The dataset included demographic details, tumor characteristics, laboratory values, treatment modalities, and follow-up outcomes. Clinical variables were converted into 2D image matrices using the IGHT. …”
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Multi-scale U-like network with attention mechanism for automatic pancreas segmentation. by Yingjing Yan, Defu Zhang

“…As an inconspicuous and small organ in the abdomen, the pancreas has a high degree of anatomical variability and is indistinguishable from the surrounding organs and tissues, which usually leads to a very vague boundary. …”
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Mitigating Cyber Risks in Smart Cyber-Physical Power Systems Through Deep Learning and Hybrid Security Models by M. A. S. P. Dayarathne, M. S. M. Jayathilaka, R. M. V. A. Bandara, V. Logeeshan, S. Kumarawadu, Chathura Wanigasekara

“…The integration of renewable energy sources, such as wind and solar, into smart grids poses operational risks due to their decentralized and variable characteristics, particularly within the communication layers essential for real-time monitoring and control. …”
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Spatiotemporal Mapping of Soil Profile Moisture in Oases in Arid Areas by Zihan Zhang, Jinjie Wang, Jianli Ding, Jinming Zhang, Li Li, Liya Shi, Yue Liu

“…The BOSS feature selection algorithm was applied to construct 46 feature parameters, including vegetation indices, soil indices, and microwave indices, and to identify optimal variable sets for each depth. Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and their hybrid model (CNN-LSTM) were used to build soil moisture inversion models at various depths. …”
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Multiscale Feature Reconstruction and Interclass Attention Weighting for Land Cover Classification by Zongqian Zhan, Zirou Xiong, Xin Huang, Chun Yang, Yi Liu, Xin Wang

“…However, high-resolution remote sensing images typically have abundant textual details, variable scales in objects, large intraclass variance, and similar interclass correlation, which bring challenges to land cover classification. …”
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FinSafeNet: securing digital transactions using optimized deep learning and multi-kernel PCA(MKPCA) with Nyström approximation by Ahmad Raza Khan, Shaik Shakeel Ahamad, Shailendra Mishra, Mohd Abdul Rahim Khan, Sunil Kumar Sharma, Abdullah AlEnizi, Osama Alfarraj, Majed Alowaidi, Manoj Kumar

“…Besides tackling feature selection and dimensionality reduction, the model presents advanced correlation measures as well as Joint Mutual Information Maximization to enhance variable correlation analysis. These improvements further help the model to detect the relevant features in transaction data that may present a threat to the security of the system. …”
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Machine Learning in the Management of Patients Undergoing Catheter Ablation for Atrial Fibrillation: Scoping Review by Aijing Luo, Wei Chen, Hongtao Zhu, Wenzhao Xie, Xi Chen, Zhenjiang Liu, Zirui Xin

“… BackgroundAlthough catheter ablation (CA) is currently the most effective clinical treatment for atrial fibrillation, its variable therapeutic effects among different patients present numerous problems. …”
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Prediction of Sea Surface Chlorophyll-a Concentrations by Remote Sensing and Deep Learning by Qingfeng Ruan, Delu Pan, Difeng Wang, Xianqiang He, Fang Gong, Qingjiu Tian

“…Current methods struggle to capture short-term variability and periodic trends in Chl-a, especially in noise-prone coastal regions. …”
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