FD²-YOLO: A Frequency-Domain Dual-Stream Network Based on YOLO for Crack Detection by Junwen Zhu, Jinbao Sheng, Qian Cai

“…However, most existing methods use multi-scale and attention mechanisms to improve on a single backbone, and this single backbone network is often ineffective in detecting slender or variable cracks in complex scenarios. We propose a novel network, FD²-YOLO, based on frequency-domain dual-stream YOLO, for accurate and efficient detection of cement cracks. …”
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Few-shot bearing fault diagnosis method based on an EEMD parallel neural network and a relation network by Cunsheng Zhao, Bo Tong, Chao Zhou, Qingrong Fan

“…Finally, the relation module of the RN was used for the nonlinear distance determination of the fault feature vector set and to generate the relation score for the few-shot variable condition bearing fault diagnosis. In this paper, EEMD module is introduced into RN to construct multi-dimensional fault characteristics of the original fault signal. …”
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Toward accurate and scalable rainfall estimation using surveillance camera data and a hybrid deep-learning framework by Fiallos-Salguero Manuel, Soon-Thiam Khu, Jingyu Guan, Mingna Wang

“…Remarkably, the model maintains strong performance during daytime and nighttime conditions, outperforming existing video-based rainfall estimation methods and demonstrating robust adaptability across variable environmental scenarios. The model's lightweight architecture facilitates efficient training and deployment, enabling practical real-time urban rainfall monitoring. …”
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STDNet: Improved lip reading via short-term temporal dependency modeling by Xiaoer Wu, Zhenhua Tan, Ziwei Cheng, Yuran Ru

“…Conclusions: The proposed model effectively addresses short-term temporal dependency limitations in lip reading, and improves the temporal robustness of the model against variable-length sequences. These advancements validate the importance of explicit short-term dynamics modeling for practical lip-reading systems.…”
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Lubricating Grease Thickness Classification of Steel Wire Rope Surface Based on GEMR-MobileViT by Ruqing Gong, Yuemin Wang, Fan Zhou, Binghui Tang

“…To achieve automated lubrication quality control and address challenges like variable lighting and motion blur that degrade recognition accuracy in practical settings, this paper proposes an improved lightweight GEMR-MobileViT. …”
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A Multi-Scale Deep Learning Framework Combining MobileViT-ECA and LSTM for Accurate ECG Analysis by Abduljabbar S. Ba Mahel, Mehdhar S. A. M. Al-Gaashani, Reem Ibrahim Alkanhel, Dina S. M. Hassan, Mohammed Saleh Ali Muthanna, Ammar Muthanna, Ahmed Aziz

“…Electrocardiogram (ECG) analysis is crucial for diagnosing cardiovascular diseases (CVD), especially atrial fibrillation (AF), a prevalent cardiac rhythm abnormality. However, the variability and complexity of ECG signals make AF classification challenging, highlighting the need for more accurate and reliable methods. …”
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Impurity rates detection for pepper harvesting based on YOLOv8n-Seg-ASB and random forest by Lijian Lu, Jin Lei, Chenming Cheng, Shiguo Wang, Chengfu Wang, Xinyan Qin

“…To address the inaccuracies and inefficiencies of pepper impurity rates detection caused by complex material compositions and variable harvesting environments, this paper proposes a detection technique based on deep and machine learning algorithms. …”
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Performance of externally validated machine learning models based on histopathology images for the diagnosis, classification, prognosis, or treatment outcome prediction in female b... by Ricardo Gonzalez, Peyman Nejat, Ashirbani Saha, Clinton J.V. Campbell, Andrew P. Norgan, Cynthia Lokker

“…Most studies used Convolutional Neural Networks and one used logistic regression algorithms. …”
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A multimodal deep learning architecture for predicting interstitial glucose for effective type 2 diabetes management by Muhammad Salman Haleem, Daphne Katsarou, Eleni I. Georga, George E. Dafoulas, Alexandra Bargiota, Laura Lopez-Perez, Miguel Rujas, Giuseppe Fico, Leandro Pecchia, Dimitrios Fotiadis, Gatekeeper Consortium

“…However, a key challenge in the effective management of type 2 diabetes lies in forecasting critical events driven by glucose variability. While recent advances in deep learning enable modeling of temporal patterns in glucose fluctuations, most of the existing methods rely on unimodal inputs and fail to account for individual physiological differences that influence interstitial glucose dynamics. …”
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Quantifying the tumour vasculature environment from CD-31 immunohistochemistry images of breast cancer using deep learning based semantic segmentation by Tristan Whitmarsh, Wei Cope, Julia Carmona-Bozo, Roido Manavaki, Stephen-John Sammut, Ramona Woitek, Elena Provenzano, Emma L. Brown, Sarah E. Bohndiek, Ferdia A. Gallagher, Carlos Caldas, Fiona J. Gilbert, Florian Markowetz

“…Current methods to measure vascular density, however, are time-consuming, suffer from high inter-observer variability and are limited in describing the complex tumour vasculature morphometry. …”
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A hybrid model for detecting motion artifacts in ballistocardiogram signals by Yuelong Jiang, Han Zhang, Qizheng Zeng

“…Various methods, including filtering techniques and machine learning approaches, have been employed to address this issue, but the challenge persists due to the complexity and variability of motion artifacts. Methods This study introduces a hybrid model for detecting motion artifacts in ballistocardiogram (BCG) signals, utilizing a dual-channel approach. …”
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Predicting the Likelihood of Operational Risk Occurrence in the Banking Industry Using Machine Learning Algorithms by Hamed Naderi, Mohammad Ali Rastegar Sorkhe, Bakhtiar Ostadi, Mehrdad Kargari

“…Capital coverage is then determined based on the cumulative distribution of these variables. Since the LDA is data-driven, the Basel framework (BCBS, 2004) emphasizes the necessity of a robust database for collecting operational risk data. …”
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An Analytical Study of Creeping Flow of a Second-Order Fluid through a Small Diameter Leaky Tube with Linearly Diminishing Absorption by Zarqa Bano, Abdul Majeed Siddiqui, Kaleemullah Bhatti

“…The obtained solution shows great similarity with the already available work in the literature. Variation in flow variables with linear absorption parameter is analysed in detail. …”
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Deep learning algorithm on H&E whole slide images to characterize TP53 alterations frequency and spatial distribution in breast cancer by Chiara Frascarelli, Konstantinos Venetis, Antonio Marra, Eltjona Mane, Mariia Ivanova, Giulia Cursano, Francesca Maria Porta, Alberto Concardi, Arnaud Gerard Michel Ceol, Annarosa Farina, Carmen Criscitiello, Giuseppe Curigliano, Elena Guerini-Rocco, Nicola Fusco

“…DL-based approaches offer significant promise for enhancing biomarker testing and precision oncology by reducing intra- and inter-observer variability, but further validation is required to optimize their integration into real-world clinical workflows. …”
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YOLOv8-MSP-PD: A Lightweight YOLOv8-Based Detection Method for Jinxiu Malus Fruit in Field Conditions by Yi Liu, Xiang Han, Hongjian Zhang, Shuangxi Liu, Wei Ma, Yinfa Yan, Linlin Sun, Linlong Jing, Yongxian Wang, Jinxing Wang

“…Accurate detection of Jinxiu Malus fruits in unstructured orchard environments is hampered by frequent overlap, occlusion, and variable illumination. To address these challenges, we propose YOLOv8-MSP-PD (YOLOv8 with Multi-Scale Pyramid Fusion and Proportional Distance IoU), a lightweight model built on an enhanced YOLOv8 architecture. …”
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Comment on S Memon, et al. (J Pak Med Assoc. 74: 1163-1166, June 2024) Osmolar gap in hyponatraemia: An exploratory study by Muhammad Ramish Irfan

“…This, alongside statistical correlation betweensuch variables, would have strengthened your argument byhighlighting a potential non-alcohol related cause for higher OGin hyponatraemic patients.Moreover, the lack of elaboration on specific characteristics ofpatients with higher OG and in particular those that sufferedmortality to further elucidate significance of OG in variousclinical contexts was also noticeable. …”
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A time-domain finite element formulation of the equivalent fluid model for the acoustic wave equation by Maurerlehner Paul, Mayrhofer Dominik, Kaltenbacher Manfred, Schoder Stefan

“…When transforming the acoustic wave equation for the EF model from the frequency domain to the time domain, convolution integrals arise. The auxiliary differential equation (ADE) method is used to circumvent the direct calculation of these convolution integrals. …”
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Deep Learning and Edge Computing in Agriculture: A Comprehensive Review of Recent Trends and Innovations by Apri Junaidi, Siti Zaiton Mohd Hashim, Mohd Shahizan Bin Othman, Mohd Murtadha Bin Mohamad, Hitham Alhussian, Said Jadid Abdulkadir, Maged Nasser, Yunusa Adamu Bena

“…Early and accurate detection of such diseases is critical to minimizing crop loss, particularly under conditions of labor shortages and climate variability. Traditional inspection methods are labor-intensive and error-prone, highlighting the need for automated, intelligent solutions. …”
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DMSF-YOLO: A Dynamic Multi-Scale Fusion Method for Maize Tassel Detection in UAV Low-Altitude Remote Sensing Images by Dongbin Liu, Jiandong Fang, Yudong Zhao

“…In the network’s backbone front, conventional convolutions are replaced with conditional parameter convolutions (CondConv) to enhance feature extraction capabilities. …”
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A Lightweight and Rapid Dragon Fruit Detection Method for Harvesting Robots by Fei Yuan, Jinpeng Wang, Wenqin Ding, Song Mei, Chenzhe Fang, Sunan Chen, Hongping Zhou

“…Dragon fruit detection in natural environments remains challenged by limited accuracy and deployment difficulties, primarily due to variable lighting and occlusions from branches. To enhance detection accuracy and satisfy the deployment constraints of edge devices, we propose YOLOv10n-CGD, a lightweight and efficient dragon fruit detection method designed for robotic harvesting applications. …”
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