Early-Stage Sensor Data Fusion Pipeline Exploration Framework for Agriculture and Animal Welfare by Devon Martin, David L. Roberts, Alper Bozkurt

“…Based on their space and time complexities, we then highlighted how these pipelines may be used for different purposes depending on the given problem. As an example, we observed that early feature extraction reduced time and space complexity in agrarian data. …”
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THE CLINICAL CONFERENCE AS GUIDANCE TO FORMING INTERNS’ PROFESSIONAL COMPETENCIES DURING FORMATION IN THE UMSA’S DEPARTMENT OF POSTGRADUATED EDUCATION OF DENTISTS by T.P. Skripnikova, M.V. Khrebor, Ju. I. Sylenko, O.A. Pisarenko

“…The main methodological feature of a clinical conference is not only the active participation of interns preparing the report, but also interaction with students. …”
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Decentralized multi-agent federated and reinforcement learning for smart water management and disaster response by H. Mancy, Naglaa E. Ghannam, Amr Abozeid, Ahmed I. Taloba

“…The centralized system used for water dynamics and disaster control usually presents itself as a scalability problem since more clients present a problem, the system's latency is high, and the system is always prone to a single-point failure. …”
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The implementation of the situational control concept of information security in automated training systems by A. M. Chernih, S. V. Fedoseev

“…The main approaches to ensuring security of information in the automated training systems are considered, need of application of situational management of security of information for the automated training systems is proved, the mathematical model and a problem definition of situational control is offered, the technique of situational control of security of information is developed.The purpose of the study. …”
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MAPM:PolSAR Image Classification with Masked Autoencoder Based on Position Prediction and Memory Tokens by Jianlong Wang, Yingying Li, Dou Quan, Beibei Hou, Zhensong Wang, Haifeng Sima, Junding Sun

“…First, MAPM designs a Masked Autoencoder (MAE) based on the transformer for pre-training, which can boost feature learning and improve classification results based on the number of labeled samples. …”
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Research on power data security full-link monitoring technology based on alternative evolutionary graph neural architecture search and multimodal data fusion by Zhenwan Zou, Bin Wang, Tao Chen, Jia Chen

“…To solve this problem, this paper proposes a hybrid method that combines multimodal data-aware attacks with Light Gradient Boosting Machine (LightGBM) and Support Vector Regression (SVR) agent models. …”
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Underground Personnel Positioning Method Based on Self-training and NLOS Suppression by SHAO Xiaoqiang, HAN Zehui, MA Bo, YANG Yongde, YUAN Zewen, LI Xin

“…[Methods] In order to solve the problem that the existing supervised learning methods for NLOS identification and suppression require long time, labor intensive feature, and high cost became of the needs to obtain training data and label allocation, a method for underground personnel positioning based on self-training and suppression of NLOS is proposed, and a new general data fusion framework is designed. …”
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LGR-Net: A Lightweight Defect Detection Network Aimed at Elevator Guide Rail Pressure Plates by Ruizhen Gao, Meng Chen, Yue Pan, Jiaxin Zhang, Haipeng Zhang, Ziyue Zhao

“…To improve the localization accuracy for small defects, we add a high-resolution small object detection layer (P2 layer) and integrate the Convolutional Block Attention Module (CBAM) to construct a four-scale feature fusion network. This study employs various data augmentation methods to construct a custom dataset for guide rail pressure plate defect detection. …”
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Point Cloud Registration for Lava Tube Surface Reconstruction Using Curvature-Optimized Projection by Jiaqi Yao, Yanqiu Wang, Wen Li, Yuan Han, Hognxu Ai, Zhenchen Ji, Fu Zheng, Zhibin Sun

“…The generation of high-precision three-dimensional models of lunar lava tubes via surface reconstruction provides technical support for subsequent exploration. To address the problem of detailed feature loss in the surface reconstruction of natural lava tubes, our study proposed a curvature-optimized point cloud surface reconstruction method. …”
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Adaptive Token Mixer for Hyperspectral Image Classification by Shuhan Lei, Meng Zhang, Yuhang Wang, Nan Tang, Ni Jia, Lihua Fu

“…However, most token selection rules overlook image patch content, often introducing task-irrelevant tokens with little valuable class distribution information. This problem is particularly severe in HSIs, which contain rich spatial and spectral information. …”
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Reliable estimation via hybrid gradient boosting machine for mud loss volume in drilling operations by Xiaozhi Lu, Farag M. A. Altalbawy, Tarak Vora, R. Manjunatha, Debasish Shit, Shirin Shomurotova, Akshay Kumar, Atreyi Pramanik, Ajay Sharma, Raed H. C. Alfilh, Samim Sherzod, Mohammad Mahtab Alam

“…Abstract Mud loss during drilling operations poses a significant problem in the oil and gas industry due to its contributions to increased costs and operational risks. …”
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SVMFN-FSAR: Semantic-Guided Video Multimodal Fusion Network for Few-Shot Action Recognition by Ran Wei, Rui Yan, Hongyu Qu, Xing Li, Qiaolin Ye, Liyong Fu

“…We utilize the Large Language Model (LLM) to expand detailed textual knowledge of various action categories, enhancing the distinction of semantic space and alleviating the problem of insufficient samples in FSAR tasks to some extent. …”
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Induction Motor Vibrations Caused by Mechanical and Magnetic Rotor Eccentricity by Goroshko A. V., Zembytska M. V., Paiuk V. P.

“…Vibration reduction of induction motors is a significant problem that requires effective models for the effects of mechanical and electromagnetic unbalanced forces. …”
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BACAD: AI-based framework for detecting vertical broken access control attacks by Ahmed Anas, Ayman A. Alhelbawy, Salwa El Gamal, Basheer Youssef

“…Furthermore, it shows that BACAD addresses the VBAC problem by presenting an applicable, dynamic, flexible, and technology-independent solution to counter VBAC vulnerability risks. …”
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Human face localization and detection in highly occluded unconstrained environments by Abdulaziz Alashbi, Abdul Hakim H.M. Mohamed, Ayman A. El-Saleh, Ibraheem Shayea, Mohd Shahrizal Sunar, Zieb Rabie Alqahtani, Faisal Saeed, Bilal Saoud

“…Furthermore, the limited availability of comprehensive datasets containing substantially obscured faces exacerbates the problem, impeding the efficacy of face detection programs. …”
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Transfer learning and the early estimation of single-photon source quality using machine learning methods by David Jacob Kedziora, Anna Musiał, Wojciech Rudno-Rudziński, Bogdan Gabrys

“…Accordingly, the study concludes by discussing future strategies for grappling with the problem of SPS context dissimilarity, e.g. feature engineering and model adaptation.…”
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Spatiotemporal analysis of ocean primary productivity in Bohai Sea estimated using improved DINEOF reconstructed MODIS data by Shuhan Jia, Linlin Bei, Yu Li, Quanhua Zhao

“…In this study, a novel multiple spatiotemporal data interpolating empirical orthogonal function (MS-DINEOF) method was employed to solve the problem of missing remote sensing data in the estimation of ocean primary productivity (OPP). …”
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Robust Classification of Smartphone Captured Handwritten Document Images Using Deep Learning by K. S. Koushik, B. J. Bipin Nair, N. Shobha Rani, Mohammed Javed

“…Document classification is a challenging research problem in Document image analysis, majorly the presence of degradations like low illumination, blur, and shadows. …”
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Limits of Solar Flare Forecasting Models and New Deep Learning Approach by G. Francisco, M. Berretti, S. Chierichini, R. Mugatwala, J. Fernandes, T. Barata, D. Del Moro

“…As a result, the model also operates independently of prior feature extraction and AR detection, thus offering promising operational utility with minimal external dependencies. …”
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A novel deep learning framework with artificial protozoa optimization-based adaptive environmental response for wind power prediction by Sangkeum Lee, Mohammad H. Almomani, Saleh Ali Alomari, Kashif Saleem, Aseel Smerat, Vaclav Snasel, Amir H. Gandomi, Laith Abualigah

“…To address these, this study proposes a novel hybrid deep learning framework, IAPO-LSTM, which combines Convolutional Neural Networks (CNNs) for spatial feature extraction and Gated Recurrent Units (GRUs) for temporal sequence modeling. …”
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