Swarm learning with weak supervision enables automatic breast cancer detection in magnetic resonance imaging by Oliver Lester Saldanha, Jiefu Zhu, Gustav Müller-Franzes, Zunamys I. Carrero, Nicholas R. Payne, Lorena Escudero Sánchez, Paul Christophe Varoutas, Sreenath Kyathanahally, Narmin Ghaffari Laleh, Kevin Pfeiffer, Marta Ligero, Jakob Behner, Kamarul A. Abdullah, Georgios Apostolakos, Chrysafoula Kolofousi, Antri Kleanthous, Michail Kalogeropoulos, Cristina Rossi, Sylwia Nowakowska, Alexandra Athanasiou, Raquel Perez-Lopez, Ritse Mann, Wouter Veldhuis, Julia Camps, Volkmar Schulz, Markus Wenzel, Sergey Morozov, Alexander Ciritsis, Christiane Kuhl, Fiona J. Gilbert, Daniel Truhn, Jakob Nikolas Kather

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Leveraging time-based spectral data from UAV imagery for enhanced detection of broomrape in sunflower by Guy Atsmon, Anna Brook, Tom Avikasis Cohen, Fadi Kizel, Hanan Eizenberg, Ran Nisim Lati

“…Results showed that the PRNN model, trained on time-series data, achieved an overall accuracy of 84.8 % and a true positive rate of 80.4 % in detecting broomrape infection, emphasizing the strength of utilizing temporal data for enhancing detection accuracies. …”
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Detecting Subtle Shifts: Comparing SME Performance Drivers Pre and During the Covid-19 Pandemic by Soza Sydney Simango, Helper Zhou

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A method for detecting abnormal electricity prices at charging stations based on two rounds of clustering by XUE Xiaohui, ZHANG Wen, ZHANG Jing, CHEN Yan, ZHOU Chun, CHEN Liang, CAO Zengwei

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Enhancing Brain Tumor Detection: A Comparative Study of CNN Architectures Using MRI Data by Zhu Zhimeng

“…Deep learning models have become essential for automated medical image analysis in brain tumor detection. Existing Convolutional Neural Network (CNN) models like Visual Geometry Group 19 (VGG19), Residual Network 18 (ResNet18), and Residual Network 34 (ResNet34), despite their success in image classification and recognition, face challenges such as unclear boundary detection, limited generalization, and lower computational efficiency in detecting brain tumors. …”
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The Relationship Between Satisfaction with Complaint Handling and Repurchase Intentions: Detecting Moderating Influences in E-Tail by Mateus Nagel, Cristiane Pizzutti dos Santos

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Advancements in Nanotechnology for Enhanced Food Safety and Hygiene: Pathogen Detection, Smart Packaging, and Preservation Applications by Farhang Hameed Awlqadr, Ammar B. Altemimi, Qausar Hamed AlKaisy, Syamand Ahmed Qadir, Aryan Mahmood Faraj, Tablo Azad H.Slih, Rawaa H. Tlay, Tarek Gamal Mohamed Abd El-Maksoud, Franscesco Cacciola

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Evaluation of Associated Structural and Chromosomal Abnormalities in Patients with Fetal Cerebral Ventriculomegaly Detected in Ultrasonographic Imaging by Erkan Cagliyan, Samican Ozmen, Sureyya Saridas Demir, Ceren Aydin, Egehan Bilen, Handan Guleryuz, Elif Yasar, Derya Ercal, Elcin Bora, Tufan Cankaya

“…Aneuploidy in ventriculomegaly is detected especially in moderate and severe ventriculomegaly accompanied by structural anomalies. …”
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Real-World Parkinson’s Hand Tremor Detection Using Ensemble Learning Techniques by Sungwook Hur, Jieming Zhang, Moon-Hyun Kim, Tai-Myoung Chung

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On-site quantitative detection of fentanyl in heroin by machine learning-enabled SERS on super absorbing metasurfaces by Yingkun Zhu, Haomin Song, Ruiying Liu, Yunyun Mu, Murali Gedda, Abdullah N. Alodhay, Lei Ying, Qiaoqiang Gan

“…This marks a key advancement in rapid, accurate fentanyl detection, aiding the fight against the opioid crisis and improving public health safety.…”
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Integrating radiological and clinical data for clinically significant prostate cancer detection with machine learning techniques by Luis Mariano Esteban, Ángel Borque-Fernando, Maria Etelvina Escorihuela, Javier Esteban-Escaño, Jose María Abascal, Pol Servian, Juan Morote

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Microgrid Islanding Detection Using D-PMU and Phase Angle Analysis of Negative Sequence Impedance by Asim Chaulagain, Ramakrishna Gokaraju, Nurul Chowdhury, Krish Narendra

“…Unintentional islanding detection is a major challenge during the operation of a microgrid. …”
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CoAt-Set: Transformed coordinated attack dataset for collaborative intrusion detection simulationMendeley Data by Aulia Arif Wardana, Grzegorz Kołaczek, Parman Sukarno

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Detectability of Liver Steatosis and Fibrosis with Transient Elastography and Controlled Attenuation Parameter in Residents of St. Petersburg by V. P. Kovyazina, K. L. Raikhelson, M. K. Prashnova, E. V. Pazenko, L. K. Palgova, E. A. Kondrashina

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Recommendations for the Detection and Mitigation of Laurel Wilt Disease in Avocado and Related Tree Species in the Home Landscape by Jonathan Henry Crane, Jeff Wasielweski, Daniel Carrillo, Romina Gazis, Bruce Schaffer, Fredy Ballen, Edward Evans

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First detection and phylogenetic analysis of Trypanosoma species in European wolves and bears: discovery of novel haplotypes by Blanka Orłowska, Magdalena Świsłocka-Cutter, Katarzyna Filip-Hutsch, Daniel Młocicki, Adam Olszewski, Marek Asman, Krzysztof Anusz, Joanna Werszko

“…The detection of Trypanosoma sp. was achieved through nested- PCR amplification. …”
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Screening to Detect Hip and Groin Problems in Elite Adolescent Football (Soccer) Players – Friend or Foe? by Matthew D DeLang, J. Craig Garrison, Kristian Thorborg

“…Injury prevention strategies in team settings should not overlook early detection and secondary prevention. Monitoring systems may be an effective approach to detect common and troublesome injuries, such as hip and groin pain in football (soccer) players. …”
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A digital twin framework with MobileNetV2 for damage detection in slab structures by Huong Duong Nguyen, Huan Nguyen, Xiaohong Gao

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Machine learning-based anomaly detection and prediction in commercial aircraft using autonomous surveillance data. by Tian Xia, Lanju Zhou, Khalil Ahmad

“…The creation of a machine learning model employing data from autonomous-reliant surveillance transmissions is essential for the detection and prediction of commercial aircraft accidents. …”
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UAV rice panicle blast detection based on enhanced feature representation and optimized attention mechanism by Shaodan Lin, Deyao Huang, Libin Wu, Zuxin Cheng, Dapeng Ye, Haiyong Weng

“…These results confirm the model’s reliability and robustness, highlighting its effectiveness in rice panicle blast detection under challenging conditions. Conclusion The ConvGAM model demonstrates strong qualitative advantages in detecting rice panicle blast disease. …”
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