Exploring CycleGAN Technique for Improved Plant Disease Detection and Analysis by Ouyang Luyi

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Brainwide Analysis of Functional Connectivity Patterns in Specific Phobia and Its Treatment by Markus Muehlhan, Judith Schäfer, Kevin Hilbert, Esther Seidl, Katja Beesdo-Baum

“…Methods: We used functional magnetic resonance imaging in combination with hypothesis-free, data-driven functional connectivity multivariate pattern analysis (fc-MVPA) to identify differences between 72 individuals with SP and a nonphobic control group (CG) (n = 82). …”
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Abrasion Resistance and Mechanism Analysis of Polyethylene Fiber-reinforced Cementitious Composite by ZI Jinjia, LI Weibin, WANG Jihong, WANG Zhiwen, LIU Yunjun, NONG Qingshun, LI Bixiong

“…The abrasion resistance was measured using the underwater steel ball method specified in SL/T 352-2020 with a HKCM-2 concrete abrasion apparatus. The point cloud data of the damaged surface was collected by a 3D laser scanner, and the average wear depth was calculated based on this data. …”
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GPS-based street-view greenspace exposure and wearable assessed physical activity in a prospective cohort of US women by Li Yi, Jaime E. Hart, Grete Wilt, Cindy R. Hu, Marcia Pescador Jimenez, Pi-I Debby Lin, Esra Suel, Perry Hystad, Steve Hankey, Wenwen Zhang, Jorge E. Chavarro, Francine Laden, Peter James

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Innovative segmentation technique for aerial power lines via amplitude stretching transform by Pengfei Xu, Nor Anis Asma Sulaiman, Yafei Ding, Jiangwei Zhao

“…Abstract Accurate segmentation of power line targets helps quickly locate faults, evaluate line conditions, and provides key image data support and analysis for the safe and stable operation of the power system.The aerial power line in segmentation due to the target is small, and the imaging reflected energy is weak, so the Unmanned Aerial Vehicle (UAV) aerial power line image is very susceptible to the interference of the environment line elements and noise, resulting in the detection of the power line target in the image of the defective, intermittent, straight line interferences and other low accuracy and real-time efficiency is not high. …”
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Video-Based Detection Infrastructure Enhancement for Automated Ship Recognition and Behavior Analysis by Xinqiang Chen, Lei Qi, Yongsheng Yang, Qiang Luo, Octavian Postolache, Jinjun Tang, Huafeng Wu

“…To bridge the gap, we proposed an ensemble you only look once (YOLO) framework for ship behavior analysis. First, we employed the convolutional neural network in the YOLO model to extract multi-scaled ship features from the input ship images. …”
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A multimodal Neuroimaging-Based risk score for mild cognitive impairment by Elaheh Zendehrouh, Mohammad S.E. Sendi, Anees Abrol, Ishaan Batta, Reihaneh Hassanzadeh, Vince D. Calhoun

“…Structural MRI (sMRI) data provided gray matter (GM) segmentation maps, while resting-state functional MRI (fMRI) data yielded functional network connectivity (FNC) matrices via spatially constrained independent component analysis. …”
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MultiModal Ensemble Approach Leveraging Spatial, Skeletal, and Edge Features for Enhanced Bangla Sign Language Recognition by Khan Abrar Shams, Md. Rafid Reaz, Mohammad Ryan Ur Rafi, Sanjida Islam, Md. Shahriar Rahman, Rafeed Rahman, Md. Tanzim Reza, Mohammad Zavid Parvez, Subrata Chakraborty, Biswajeet Pradhan, Abdullah Alamri

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Trend Detection and Forecasting of LST in Tabriz City using the Non-parametric Mann-Kendall and NNAR by Mohammad Ali Koushesh Vatan, Akbar Asghari Zamani, Shahrivar Rostaei

“…Material & Method: The LST data were extracted from the MODIS satellite images for both day and night. …”
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Artificial intelligence in neurodegenerative diseases research: a bibliometric analysis since 2000 by Yabin Zhang, Lei Yu, Yuting Lv, Tiantian Yang, Qi Guo

“…High-frequency keywords include “alzheimer’s disease,” “parkinson’s disease,” “magnetic resonance imaging,” “convolutional neural network,” “biomarkers,” “dementia,” “classification,” “mild cognitive impairment,” “neuroimaging,” and “feature extraction.” …”
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Small Lesions Evaluation Based on Unsupervised Cluster Analysis of Signal-Intensity Time Courses in Dynamic Breast MRI by A. Meyer-Baese, T. Schlossbauer, O. Lange, A. Wismueller

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Transformer-based transfer learning on self-reported voice recordings for Parkinson’s disease diagnosis by Ilias Tougui, Mehdi Zakroum, Ouassim Karrakchou, Mounir Ghogho

“…Transfer learning was applied by starting with models pre-trained on large datasets from the image and the audio domains and then fine-tuning them on the mPower voice data. …”
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Deep learning-driven brain tumor classification and segmentation using non-contrast MRI by Nan-Han Lu, Yung-Hui Huang, Kuo-Ying Liu, Tai-Been Chen

“…Non-contrast T1-weighted (T1w) and T2-weighted (T2w) images were combined with their average to form RGB three-channel inputs, enriching the representation for model training. …”
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Application of Artificial Intelligence in Retinopathy of Prematurity From 2010 to 2023: A Bibliometric Analysis by Jing Gao, Na Fang, Yao Xu

“…Data analysis and visualization were performed using Bibliometrix and VOSviewer, covering publications, journals, authors, institutions, countries, collaboration networks, keywords, and trending topics. …”
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Near-Infrared Hyperspectral Target Tracking Based on Background Information and Spectral Position Prediction by Li Wu, Mengyuan Wang, Weixiang Zhong, Kunpeng Huang, Wenhao Jiang, Jia Li, Dong Zhao

“…This module performs spectral matching on the history frame images through the average spectral curve of the groundtruth value of the target and makes a rough distinction between the target and the background. …”
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Predicting Sugarcane Yield Through Temporal Analysis of Satellite Imagery During the Growth Phase by Julio Cezar Souza Vasconcelos, Caio Simplicio Arantes, Eduardo Antonio Speranza, João Francisco Gonçalves Antunes, Luiz Antonio Falaguasta Barbosa, Geraldo Magela de Almeida Cançado

“…This research investigates how to estimate sugarcane (<i>Saccharum officinarum</i> L.) yield at harvest by using an average satellite image time-series collected during the growth phase. …”
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Enhanced Rolling Bearing Fault Diagnosis Using Multimodal Deep Learning and Singular Spectrum Analysis by Yunhang Wang, Hongwei Wang, Ruoyang Bai, Yuxin Shi, Xicong Chen, Qingang Xu

“…Based on this, a recursive gated convolutional neural network (RGCNN) is designed to process the STFT image data, while a 1D convolutional neural network (1DCNN) is specifically optimized for training with time series data. …”
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MRTMD: A Multi-Resolution Dataset for Evaluating Object Detection in Traffic Monitoring Systems by Mark Bugeja, Matthias Bartolo, Matthew Montebello, Dylan Seychell

“…However, their effectiveness depends heavily on the quality of the input data, especially image resolution. With high-resolution cameras, especially 4K, balancing image quality, detection accuracy, and system efficiency is critical. …”
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Deep learning-based dual optimization framework for accurate thyroid disease diagnosis using CNN architectures by Zeeshan Ali Haider, Nasser A Alsadhan, Fida Muhammad Khan, Waleed Al-Azzawi, Inam Ullah Khan, Inam Ullah

“…Further research will also explore real-time ultrasound analysis and multi-modal data integration, such as combining medical images with patient history, to enhance diagnostic accuracy. …”
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An Improved U-Net-Based Framework for Estimating River Surface Flow Velocity by 周继威, 安国成, 王根一

“…This study aimed to address these limitations by developing a robust framework that integrates improved data labeling strategies, optimized training methods, and a novel neural network architecture to achieve high-precision velocity estimation. …”
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