Deep-Learning-Based Solar Flare Prediction Model: The Influence of the Magnetic Field Height by Lei Hu, Zhongqin Chen, Long Xu, Xin Huang

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Fish oocyte morphology detection using neural networks: a comparison of YOLO architectures by Yanna Leidy Ketley Fernandes Cruz, Ewaldo Eder Carvalho Santana, Isa Rosete Araujo Nascimento, Antonio Fhillipi Maciel Silva, Raimunda Nonata Fortes Carvalho Neta, José Ribamar de Souza Torres-Junior

“…The research uses an image database with 5,680 oocytes with different maturation stadiums (PV - pre-vitellogenesis, VI - early vitellogenesis and VF - late vitellogenesis), in histological images, divided into training, testing and validation, and detection performed by YOLOv3, YOLOv4, and YOLOv5 architectures. …”
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Detection of single and dual pulmonary diseases using an optimized vision transformer by Almughamisi Naif, Abosamra Gibrael, Albar Adnan, Saleh Mostafa

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Estimating the effect of age-induced defocus on object detection performance of automotive cameras by Amit Pandey, Stephan Kühn, Marcel Kettelgerdes, Bernhard Wunderle, Gordon Elger

“…This work investigates the effect of defocus on safety-critical object detection by comparing various pre-trained object detection models, primarily convolutional neural networks. These models are compared in their performance against static scenes with different levels of defocus. …”
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Deep Learning Algorithm Analysis of Potato Disease Classification for System on Chip Implementation by John Adebisi, Sesham Srinu, Varqa Mitonga

“…The broad range of crops has witnessed setbacks in different capacities due to climate change among other factors, hence leading to diseases and infections; thereby leading to negatively impacted nutrition. …”
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Deep Learning-Based Multistage Fire Detection System and Emerging Direction by Tofayet Sultan, Mohammad Sayem Chowdhury, Mejdl Safran, M. F. Mridha, Nilanjan Dey

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A Unified Approach to Voice Classification: Leveraging Spectrograms, Mel Spectrograms, and Statistical Features by Muhammad Talha, Huma Ghafoor, Seung Yeob Nam

“…These findings confirm the effectiveness of the proposed multimodal architecture for accurate voice and sound classification across different datasets.…”
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PLANT DISEASE DETECTION USING DEEP LEARNING by Amrita, Sanjay Kumar Nayak, Rajiv Kumar, Sakshi Tomar

“…In this paper, several deep learning (DL) models are proposed to recognize the multiple classes of diseases present in plants from the images of leaves taken under various resolutions and different environmental conditions. Employing a Deep Convolutional Neural Network (CNN) in multi-class classification for detecting plant diseases can be beneficial in the early identification of these diseases and also in dealing with the negative impact of these diseases on agriculture. …”
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Triplet-Style Dynamic Graph Network With Transformer Encoder for Scam Detection in Cryptocurrency Transactions by Min-Woo Nam, Hyeon-Ju Lee, Seok-Jun Buu

“…TD-GCN’s performance gains stem from its dynamic updates for evolving networks and Triplet Learning for disentangling subtly different patterns. These features enable TD-GCN to significantly bolster cryptocurrency security by effectively detecting scams and minimizing false positives in dynamic transaction networks.…”
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High sensitivity in spontaneous intracranial hemorrhage detection from emergency head CT scans using ensemble-learning approach by Juuso Takala, Heikki Peura, Riku Pirinen, Katri Väätäinen, Sergei Terjajev, Ziyuan Lin, Rahul Raj, Miikka Korja

“…The DL solution included four base convolutional neural networks (CNNs), which were trained using 300 head CT scans. …”
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SLG-Net: Small-Large-Global Feature-Based Multilevel Feature Extraction Network for Ultrasound Image Segmentation by Xinya Fan, Jianwen Hu, Kai Hu

“…Specifically, the CNN encoder improves the representation and interaction of fine feature and large-scale context feature for targets of different sizes by large-small kernel attention (LSKA) modules. …”
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Assessment of a Smartphone-Based Neural Network Application for the Risk Assessment of Skin Lesions under Real-World Conditions by Teresa Maria Kränke, Philipp Efferl, Katharina Tripolt-Droschl, Rainer Hofmann-Wellenhof

“… Introduction: The diagnostic performance of convolutional neural networks (CNNs) in diagnosing different types of skin cancer has been quite promising. …”
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Application of Hyperspectral Imaging for Identification of Melon Seed Variety Using Deep Learning by Zhiqi Hong, Chu Zhang, Wenjian Song, Xiangbo Nie, Hongxia Ye, Yong He

“…The image features of the corresponding seeds were manually extracted from the RGB images. Five different datasets were formed using the spectral features and RGB images of the seeds, including seed spectral features, manually extracted seed image features, seed images, the fusion of seed spectral features with manually extracted features, and the fusion of seed spectral features with seed images. …”
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A Machine Learning Free Energy Functional for the 1D Reference Interaction Site Model: Towards Prediction of Solvation Free Energy for All Solvent Systems by Jonathan G. M. Conn, Abdullah Ahmad, David S. Palmer

“…In this work, we show that a single machine learning free energy functional for RISM can accurately model solvation thermodynamics in multiple solvents. A convolutional neural network is trained on solvation free energy density functions calculated by RISM for small organic molecules in approximately 100 different solvent systems. …”
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PolyNet: A self-attention based CNN model for classifying the colon polyp from colonoscopy image by Khaled Eabne Delowar, Mohammed Borhan Uddin, Md Khaliluzzaman, Riadul Islam Rabbi, Md Jakir Hossen, M. Moazzam Hossen

“…This study provides a sensitivity analysis to demonstrate how slight modifications in the network's architecture can impact the balance between accuracy and performance. We examined different CNN architectures and developed a good convolutional neural network (CNN) model for correctly predicting colon polyps using the Kvasir dataset. …”
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Image Classification Model Based on Contrastive Learning With Dynamic Adaptive Loss by Quandeng Gou, Jingxuan Zhou, Zi Li, Fangrui Zhang, Yuheng Ren

“…Most existing mainstream image classification models use the Convolutional Neural Network (CNN), the Transformer, or a combination of both as the backbone. …”
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ANF-Net: A Refined Segmentation Network for Road Scenes with Multiple Noises and Various Morphologies of Cracks by Xiao Hu, Qihao Chen, Xiuguo Liu, Gang Deng, Cheng Chi, Bin Wang

“…On the other hand, a constrained multi-morphological convolution structure is constructed by imposing learnable continuous constraints on the deformation offsets of convolutional kernels, allowing the network to adaptively fit different crack shapes. …”
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Hand Gesture Recognition From Wrist-Worn Camera for Human–Machine Interaction by Hong-Quan Nguyen, Trung-Hieu Le, Trung-Kien Tran, Hoang-Nhat Tran, Thanh-Hai Tran, Thi-Lan Le, Hai Vu, Cuong Pham, Thanh Phuong Nguyen, Huu Thanh Nguyen

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A Deep Learning–Based Multimodal F10.7 Prediction with Mamba by Dong Zhao, Fengping Dou, Xueshang Feng, Xin Huang, Sai Ma, Long Xu

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Future variation and uncertainty source decomposition in deep learning bias-corrected CMIP6 global extreme precipitation historical simulation by Xiaohua Xiang, Yongxuan Li, Xiaoling Wu, Zhu Liu, Lei Wu, Biqiong Wu, Chuanxin Jin, Zhiqiang Zeng

“…In addition, this study endeavors to separate and quantify three different components of uncertainty (model uncertainty, scenario uncertainty, and internal variability) associated with ETCCDI extreme precipitation indices and evaluate the impact of bias correction on uncertainty variation. …”
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