Early monitoring of drought stress in safflower (Carthamus tinctorius L.) using hyperspectral imaging: A comparison of machine learning tools and feature selection approaches by Fatemeh Salek, Seyed Ahmad Mireei, Abbas Hemmat, Mehrnoosh Jafari, Mohammad R. Sabzalian, Majid Nazeri, Wouter Saeys

“…Early detection of drought stress is essential for preventing permanent plant damage and minimizing yield loss. …”
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FEBRILE INFECTION-RELATED EPILEPSY SYNDROME (FIRES) IN SCHOOLCHILDREN: LITERATURE REVIEW AND OWN OBSERVATIONS by S. L. Kulikova, S. A. Likhachev, S. A. Belaya, T. V. Svinkovskaya, A. R. Kashyna

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Generating Synthetic Datasets with Deep Learning Models for Human Physical Fatigue Analysis by Arsalan Lambay, Ying Liu, Phillip Morgan, Ze Ji

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AI-CMCA: a deep learning-based segmentation framework for capillary microfluidic chip analysis by Mahmood Khalghollah, Azam Zare, Esmaeil Shakeri, Behrouz Far, Amir Sanati-Nezhad

“…Here, we present AI-CMCA, an artificial intelligence framework designed for capillary microfluidic chip analysis, which automates fluid path detection and tracking using deep learning-based segmentation. …”
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EmoNet: Deep Attentional Recurrent CNN for X (Formerly Twitter) Emotion Classification by Md. Shakil Hossain, Md. Mithun Hossain, Md. Shakhawat Hossain, M. F. Mridha, Mejdl Safran, Sultan Alfarhood

“…Emotion classification from social media data is critical for market research, sentiment analysis, and understanding human behavior, yet the unstructured nature of Twitter data poses significant challenges for conventional models. …”
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Enhancing Security in DNP3 Communication for Smart Grids: A Segmented Neural Network Approach by Shahid Allah Bakhsh, Muhammad Shahbaz Khan, Oumaima Saidani, Nada Alasbali, S. Qasim Abbas, Muhammad Almas Khan, Jawad Ahmad

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Multi-Type Change Detection and Distinction of Cultivated Land Parcels in High-Resolution Remote Sensing Images Based on Segment Anything Model by Zhongxin Huang, Xiaomei Yang, Yueming Liu, Zhihua Wang, Yonggang Ma, Haitao Jing, Xiaoliang Liu

“…By performing spatial connection analysis on cultivated land parcel units extracted by the SAM for two phases and combining multiple features such as texture features (GLCM), multi-scale structural similarity (MS-SSIM), and normalized difference vegetation index (NDVI), precise identification of cultivation type and pattern change areas was achieved. …”
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Robust predictive framework for diabetes classification using optimized machine learning on imbalanced datasets by Inam Abousaber, Haitham F. Abdallah, Hany El-Ghaish

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Machine-Learning-Assisted Nanozyme-Based Sensor Arrays: Construction, Empowerment, and Applications by Jinjin Liu, Xinyu Chen, Qiaoqiao Diao, Zheng Tang, Xiangheng Niu

“…With the catalytic signal amplification feature, nanozymes not only find wide use in traditional “lock-and-key” single-target detection but hold great potential in high-throughput multiobjective analysis via fabricating sensor arrays. …”
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Enhancing Structural Health Monitoring of Super-Tall Buildings Using Support Vector Machines, MEMD, and Wavelet Transform by Rouzbeh Doroudi, Seyed Hossein Hosseini Lavasani, Mohsen Shahrouzi, Aref Afshar

“…MEMD interprets signals well, allowing simultaneous analysis of multiple signals, while WT eliminates noise from acceleration response data, enhancing damage detection accuracy. …”
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Deep learning with ensemble-based hybrid AI model for bipolar and unipolar depression detection using demographic and behavioral based on time-series data by Naga Raju Kanchapogu, Sachi Nandan Mohanty

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Enhancing Gait Analysis for Parkinson’s Disease Detection and Severity Staging With a Parallel Conv1D-Efficient Transformer and Bidirectional GRU Hybrid Architecture by Xiaoli Huan, Hong Zhou, Byungkwan Jung, Long Ma

“…This study introduces a novel parallel hybrid architecture combining Conv1D, Efficient Transformers, and Bidirectional GRU layers to analyze gait data for both PD detection and severity staging. Conv1D layers extract local spatial features, Efficient Transformers capture contextual dependencies, and Bidirectional GRUs model temporal patterns in VGRF signals. …”
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A Novel Approach for Automatic Detection of Concrete Surface Voids Using Image Texture Analysis and History-Based Adaptive Differential Evolution Optimized Support Vector Machine by Nhat-Duc Hoang, Quoc-Lam Nguyen

“…To inspect the quality of concrete structures, surface voids or bugholes existing on a concrete surface after the casting process needs to be detected. To improve the productivity of the inspection work, this study develops a hybrid intelligence approach that combines image texture analysis, machine learning, and metaheuristic optimization. …”
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Clinical and epidemiological features of respiratory syncytial infection in children of different age by V. B. Rovniy, Yu. V. Lobzin, I. V. Babachenko, O. M. Ibragimova, E. S. Romanova

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A New Approach Based on Metaheuristic Optimization Using Chaotic Functional Connectivity Matrices and Fractal Dimension Analysis for AI-Driven Detection of Orthodontic Growth and D... by Orhan Cicek, Yusuf Bahri Özçelik, Aytaç Altan

“…However, the nonlinear dynamics of these images pose significant challenges for reliable detection. This study presents a novel approach that integrates chaotic functional connectivity (FC) matrices and fractal dimension analysis to address these challenges. …”
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Optimized Two-Stage Anomaly Detection and Recovery in Smart Grid Data Using Enhanced DeBERTa-v3 Verification System by Xiao Liao, Wei Cui, Min Zhang, Aiwu Zhang, Pan Hu

“…The first stage employs an optimized increment-based detection algorithm achieving 95.0% for recall and 54.8% for precision through multidimensional analysis. …”
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Features of the course and therapy of HIV infection in children at different stages of the disease by J. C. Hakizmana, E. B. Yastrebova, V. N. Timchenko, D. A. Gusev, O. V. Bulina

“…Purpose of the study. Analysis of clinical and laboratory features of the course of HIV infection and antiviral therapy in children at different stages of the disease.Materials and methods. …”
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Features of Diseases of the Anterior Part of the Eye in Patients after COVID-19 by E. V. Yani, E. N. Orlova, K. E. Seliverstova

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GAF-GradCAM: Guided dynamic weighted fusion of temporal and frequency GAF 2D matrices for ECG-based arrhythmia detection using deep learning by Zakaria Khatar, Dounia Bentaleb, Noreddine Abghour, Khalid Moussaid

“…Optimizing this fusion process fine-tunes the balance between temporal and frequency information, thus focusing the model on the most critical ECG features. As a result, training accuracy reached 99.68% and validation accuracy 98.78%, alongside a substantial reduction in loss, underscoring the efficacy of Grad-CAM-guided fusion in integrating essential ECG features and advancing arrhythmia detection accuracy. …”
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Features of management strategy for the implementation of innovations aimed at improving the competitiveness of the enterprise by V. S. Alieva

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