Plant Stress Detection Using a Three-Dimensional Analysis from a Single RGB Image by Madaín Pérez-Patricio, J. A. de Jesús Osuna-Coutiño, German Ríos-Toledo, Abiel Aguilar-González, J. L. Camas-Anzueto, N. A. Morales-Navarro, J. Renán Velázquez-González, Luis Ángel Cundapí-López

“…Second, we propose a leaf detection analysis to classify and locate the boundaries between the different leaves. …”
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Harnessing Semantic and Trajectory Analysis for Real-Time Pedestrian Panic Detection in Crowded Micro-Road Networks by Rongyong Zhao, Lingchen Han, Yuxin Cai, Bingyu Wei, Arifur Rahman, Cuiling Li, Yunlong Ma

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Systematic Review and Meta-Analysis on the Accuracy of Artificial Intelligence Algorithms in Individuals Gender Detection Using Orthopantomograms by Mahmood Dashti, Tara Azimi, Farshad Khosraviani, Sarina Azimian, Lina Bahanan, Houyar Zahmatkesh, Heba Ashi, Zohaib Khurshid

“…However, there was variability in sensitivity across different studies, indicating that some models are more reliable than others depending on the dataset and features used. The funnel plot analysis suggested slight asymmetry, indicating potential publication bias or heterogeneity. …”
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TDA SegUNet: Topological Data Analysis-Based Shape-Aware Brain Tumor Segmentation by Ansar Rahman, Ayesha Satti, Ahmad Raza Shahid, Qaisar M. Shafi, Khadija Farooq, Asad Ali Safi

“…TDA-SegUNet is a U-Net-based segmentation model that integrates topological data analysis (TDA) to extract shape-based local and global features from MRI scans. …”
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Acoustic signatures of bulbar ALS: Predictive modeling with sustained vowels and LightGBM by Zahra Farrokhi, Seyed Amirali Zakavi, Arian Sarafraz, Maryam Valifard, Salar Yousefzadeh, Zahra Mashhadi Tafreshi, Omid Anbiyaee, Navid Rostami, Mahsa Asadi Anar, Niloofar Deravi

“…Background: Amyotrophic Lateral Sclerosis (ALS) is a degenerative neurologic disease with no definitive biomarkers for early detection. This paper discusses the use of acoustic analysis of sustained vowel phonations (SVP) and machine learning in ALS detection. …”
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Automatic Blob Detection Method for Cancerous Lesions in Unsupervised Breast Histology Images by Vincent Majanga, Ernest Mnkandla, Zenghui Wang, Donatien Koulla Moulla

“…The early detection of cancerous lesions is a challenging task given the cancer biology and the variability in tissue characteristics, thus rendering medical image analysis tedious and time-inefficient. …”
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Features of neuroglia at the epicenter of spinal cord contusion injury and at distant areas in mini-pigs by R. R. Garifulin, A. A. Izmailov, V. A. Markosyan, I. S. Minyazeva, V. V. Valiullin, R. R. Islamov

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A Novel Convolutional Neural Network–Long Short-term Memory Model for Interplanetary Coronal Mass Ejection Detection by Junpei Li, Hong Chen, Jingjing Wang, Yanhong Chen, Bingxian Luo, Hao Deng

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Detection of AI-Generated Texts: A Bi-LSTM and Attention-Based Approach by John Blake, Abu Saleh Musa Miah, Krzysztof Kredens, Jungpil Shin

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Features of cytogenetic diagnosis of simultaneous chronic lymphocytic leukemia and myelodysplastic syndrome: clinical case report by M. A. Kislitsyna, T. N. Obukhova, A. V. Kokhno, L. A. Grebenyuk, A. V. Luchkin, L. A. Kuzmina, V. N. Dvirnyk, A. M. Kovrigina, I. V. Galtseva, B. V. Biderman, V. V. Troitskaya, E. N. Parovichnikova

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Tissue Counter Analysis of Histologic Sections of Melanoma: Influence of Mask Size and Shape, Feature Selection, Statistical Methods and Tissue Preparation by Josef Smolle, Armin Gerger, Wolfgang Weger, Heinz Kutzner, Michael Tronnier

“…Background: Tissue counter analysis is an image analysis tool designed for the detection of structures in complex images at the macroscopic or microscopic scale. …”
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Deep Learning-Based Real-Time Driver Cognitive Distraction Detection by Matteo Fresta, Francesco Bellotti, Igor Bochenko, Luca Lazzaroni, Gaetan Merlhiot, Fabio Tango, Riccardo Berta

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Multi-model machine learning framework for lung cancer risk prediction: A comparative analysis of nine classifiers with hybrid and ensemble approaches using behavioral and hematolo... by Vinod Kumar, Chander prabha, Deepali Gupta, Sapna Juneja, Swati Kumari, Ali Nauman

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Novel Deep Learning-Based Facial Forgery Detection for Effective Biometric Recognition by Hansoo Kim

“…This study presents a novel deep learning framework optimized for texture analysis to detect facial forgeries effectively. The proposed method leverages high-frequency texture features, such as roughness, color variation, and randomness, which are more challenging to replicate than specific facial features. …”
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Self-Supervised Drift-Resilient Classification for Time Series Industrial Anomaly Detection by Myung-Kyo Seo, Byeong Hoon Yoon, Junseung Ryu, Hyung Ju Hwang

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Influence of rhythm features on beat/movement synchronization using a low-cost vision system by Hamza Bayd, Patrice Guyot, Benoît Bardy, Pierre Slangen

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Lameness Recognition of Dairy Cows Based on Compensation Behaviour Analysis by Swing and Posture Features from Top View Depth Image by Ruihong Zhang, Kaixuan Zhao, Jiangtao Ji, Jinjin Wang

“…Based on motion trajectory analysis of 655 samples (258 sound, 267 mild lameness, and 130 severe lameness), the stability mechanisms of back movement posture were investigated, compensatory behaviours in lame cows were revealed, and methods for extracting CMFs were established, including swing and posture features. …”
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Comparative Analysis of AlexNet, ResNet-50, and VGG-19 Performance for Automated Feature Recognition in Pedestrian Crash Diagrams by Baraah Qawasmeh, Jun-Seok Oh, Valerian Kwigizile

“…This highlights the critical importance of choosing the appropriate architecture for crash diagram analysis, particularly in the context of pedestrian safety. …”
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Enhancing Hazard Detection and Risk Severity Assessment in Construction through Multinomial Naive Bayes and Regression by Akaninyene Michael Akwaisua, Anietie Ekong, Godwin Ansa

“…Comprising over 100,000 instances, it captures various hazard categories prevalent in construction sites, providing a comprehensive foundation for predictive analysis. In the first phase of the study, the system is designed to detect hazards present in construction sites. …”
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Analysis of the spatial distribution of the Siberian silk moth outbreak area based on terrain features in the Siberian mountain southern taiga forests by Svetlana M. Sultson, Andrey A. Goroshko, Denis A. Demidko, Pavel V. Mikhaylov, Olga A. Slinkina, Nadezhda N. Kulakova

“…The study was carried out by the method of retrospective analysis based on a geoinformation system combined with data from remote sensing of the Earth. …”
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