Deep learning approaches for estimating maximum wall deflection in excavations with inconsistent clay stratigraphy by Vinh V. Le, HongGiang Nguyen, Nguyen Huu Ngu

“…Based on the feature importance analysis from model weights, excavation depth, stiffness ratio, and bracing spacing were ranked as the highest contributors. …”
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Multi-Agent Hierarchical Graph Attention Actor–Critic Reinforcement Learning by Tongyue Li, Dianxi Shi, Songchang Jin, Zhen Wang, Huanhuan Yang, Yang Chen

“…Specifically, graph neural networks encode agent observations as single feature-embedding vectors, maintaining a constant dimensionality irrespective of the number of agents, which improves model scalability. …”
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Abstract visual reasoning based on algebraic methods by Mingyang Zheng, Weibing Wan, Zhijun Fang

“…The model incorporates a relational bottleneck method from information theory, separating the input perceptual information from the embeddings of abstract representations, thereby restricting and differentiating feature processing to encourage relational comparisons and induce the extraction of abstract patterns. …”
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Electric Submersible Pump Fault Diagnosis Based on Laplacian Eigenmaps and Weighted Extreme Learning Machine by XU Zekun, FU Jun, GAO Xiaoyong, ZHANG Yu, LI Qiang, TAN Chaodong

“…In response to the serious imbalance in the data collected by ESP, firstly, a fault diagnosis model is established using a weighted extreme learning machine; Then, to solve the problems of insufficient algorithm learning, high computational costs caused by weighted strategies, and poor performance in applying to high-dimensional feature spaces, the Laplacian eigenmaps method is introduced to further optimize the model; finally, the effectiveness of the proposed method was validated on the TE chemical process dataset, and the practicality of the algorithm was experimentally validated on the real-time fault dataset of electric submersible pump. …”
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Artificial Intelligence-Driven Multimodal Route Planning: Addressing Dynamic Unavailability and Disruptions by Surya Prakash, Utkal Mehta, Bibhya Sharma

“…Validation through various experiments is done to demonstrate the model’s performance in accurately predicting routes, particularly in scenarios involving route unavailability. …”
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Leveraging airport CCTV footage through video understanding techniques for visibility prediction by Zeonlung Pun, Xinyu Tian, Shan Gao

“…Unlike traditional static image-based methods, our proposed three-stream network model integrates spatial information from individual frames, motion dynamics through optical flow, and key reference points extracted via SIFT (scale-invariant feature transform) descriptors. …”
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Distributed Photovoltaic Short-Term Power Prediction Based on Personalized Federated Multi-Task Learning by Wenxiang Luo, Yang Shen, Zewen Li, Fangming Deng

“…The federal learning framework is used to enhance the privacy of photovoltaic data and improve the model’s performance in a distributed environment. …”
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A Lightweight Anti-Unmanned Aerial Vehicle Detection Method Based on Improved YOLOv11 by Yunlong Gao, Yibing Xin, Huan Yang, Yongjuan Wang

“…The experimental results show that the improved method we propose has better performance in anti-UAV detection tasks, and the model is more lightweight.…”
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Explainable AI for Lightweight Network Traffic Classification Using Depthwise Separable Convolutions by Mustafa Ghaleb, Mosab Hamdan, Abdulaziz Y. Barnawi, Muhammad Gambo, Abubakar Danasabe, Saheed Bello, Aliyu Habib

“…To overcome these limitations, we introduce a lightweight NTC model based on Depthwise Separable Convolutions and compare its performance against CNN, RNN, and state-of-the-art models. …”
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Deep Learning Based Automatic Ankle Tenosynovitis Quantification from MRI in Patients with Psoriatic Arthritis: A Feasibility Study by Saeed Arbabi, Vahid Arbabi, Lorenzo Costa, Iris ten Katen, Simon C. Mastbergen, Peter R. Seevinck, Pim A. de Jong, Harrie Weinans, Mylène P. Jansen, Wouter Foppen

“…Model performance was evaluated using Dice scores. …”
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Polygenic prediction of human longevity on the supposition of pervasive pleiotropy by M. Reza Jabalameli, Jhih-Rong Lin, Quanwei Zhang, Zhen Wang, Joydeep Mitra, Nha Nguyen, Tina Gao, Mark Khusidman, Sanish Sathyan, Gil Atzmon, Sofiya Milman, Jan Vijg, Nir Barzilai, Zhengdong D. Zhang

“…Abstract The highly polygenic nature of human longevity renders pleiotropy an indispensable feature of its genetic architecture. Leveraging the genetic correlation between aging-related traits (ARTs), we aimed to model the additive variance in lifespan as a function of the cumulative liability from pleiotropic segregating variants. …”
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Intelligent Assessment of Personal Credit Risk Based on Machine Learning by Chuansheng Wang, Hang Yu

“…Then, the XGBoost algorithm is used to evaluate the credit risk level of customers, and the traditional Sparrow Search Algorithm is improved by using Tent chaotic mapping, sine and cosine search, reverse learning, and Cauchy mutation strategy to improve the optimization performance of algorithm parameters. Finally, using the Lending Club dataset for empirical analysis, the experiment shows that the model improves the accuracy of personal credit risk assessment and enhances the ability of risk control.…”
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A Machine Learning Approach to Credit Card Transaction Fraud Prediction by Liu Zixuan

“…By conducting data processing techniques such as feature scaling and dataset balancing, the model shows an effective identification of fraudulent transactions that rarely exist in a vast number of legitimate transactions. …”
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Low frequency oscillations – neural correlates of stability and flexibility in cognition by Julia Ericson, Nieves Ruiz Ibáñez, Mikael Lundqvist, Torkel Klingberg

“…The model, featuring spiking and oscillatory cortical layers interacting via phase-amplitude coupling, demonstrated how frequency and spatial region could modulate information flow. …”
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Automated Detection of Tailing Impoundments in Multi-Sensor High-Resolution Satellite Images Through Advanced Deep Learning Architectures by Lin Qin, Wenyue Song

“…An upgraded You Only Look Once (YOLO) model is introduced, integrating three key innovations: a multi-scale feature aggregation layer, a lightweight hierarchical fusion mechanism, and a modified loss metric. …”
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A novel data processing approach to detect fraudulent insurance claims for physical damage to cars by Ahmet Yücel

“…For all models, the models with composite variables achieved higher accuracy rates, and among these models, the artificial neural network was the model with the highest accuracy performance at 76.56%.…”
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基于现代分析技术对准双曲面齿轮进行加载接触分析 by 蒋闯, 邓效忠, 张华

“…Based on the theory of HFT（hypoid gear formate tilt） for Manufacturing hypoid gears,according to the feature of formate for gears and Generate for pinions,coordinate systems are built for generating pair of gears,then the equations of tooth,coign and fillet of Gears are obtained.The discrete points of pair of gears are calculated by MATLAB,and the accurate geometrical models can be built easily in UG,cutting three teeth of the pair of gears for HpyerMesh and establishing finite element model,we can acquire the distribute of contact stress of the meshing teeth in ANSYS,this will lay the foundation of meshing performance analysis in the nature of things.…”
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Red Raspberry Maturity Detection Based on Multi-Module Optimized YOLOv11n and Its Application in Field and Greenhouse Environments by Rongxiang Luo, Xue Ding, Jinliang Wang

“…Concurrently, the DWR module optimizes the learning process through residual connections, thereby enhancing the accuracy and real-time performance of the model. Finally, a lightweight and efficient dynamic upsampling module (DySample) is introduced between the backbone and neck of the network. …”
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Enhancing advanced cervical cell categorization with cluster-based intelligent systems by a novel integrated CNN approach with skip mechanisms and GAN-based augmentation by Gunjan Shandilya, Sheifali Gupta, Ahmad Almogren, Salil Bharany, Ayman Altameem, Ateeq Ur Rehman, Seada Hussen

“…The model gives an excellent performance because it has a validation accuracy of 99.11% and a training accuracy of 99.82%. …”
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CCTNet: CNN and Cross-Shaped Transformer Hybrid Network for Remote Sensing Image Semantic Segmentation by Honglin Wu, Zhaobin Zeng, Peng Huang, Xinyu Yu, Min Zhang

“…Deep learning methods have achieved great success in the field of remote sensing image segmentation in recent years, but building a lightweight segmentation model with comprehensive local and global feature extraction capabilities remains a challenging task. …”
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