YOLOv8s-Longan: a lightweight detection method for the longan fruit-picking UAV by Jun Li, Jun Li, Jun Li, Kaixuan Wu, Meiqi Zhang, Hengxu Chen, Hengyi Lin, Yuju Mai, Linlin Shi

“…IntroductionDue to the limited computing power and fast flight speed of the picking of unmanned aerial vehicles (UAVs), it is important to design a quick and accurate detecting algorithm to obtain the fruit position.MethodsThis paper proposes a lightweight deep learning algorithm, named YOLOv8s-Longan, to improve the detection accuracy and reduce the number of model parameters for fruitpicking UAVs. …”
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MambaShadowDet: A High-Speed and High-Accuracy Moving Target Shadow Detection Network for Video SAR by Xiaowo Xu, Tianwen Zhang, Xiaoling Zhang, Wensi Zhang, Xiao Ke, Tianjiao Zeng

“…To handle these issues, this paper proposes MambaShadowDet, a novel lightweight deep learning (DL) detector based on a state space model (SSM), dedicated to high-speed and high-accuracy moving target shadow detection in video SAR images. …”
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Astronomaly Protege: Discovery through Human-machine Collaboration by Michelle Lochner, Lawrence Rudnick

“…We train PROTEGE on images from the MeerKAT Galaxy Cluster Legacy Survey, leveraging the self-supervised deep learning algorithm Bootstrap Your Own Latent to find a low-dimensional representation of the radio galaxy cutouts. …”
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Investigating the intrinsic top-down dynamics of deep generative models by Lorenzo Tausani, Alberto Testolin, Marco Zorzi

“…A popular class of hierarchical generative models is that of Deep Belief Networks (DBNs), which are energy-based deep learning architectures that can learn multiple levels of representations in a completely unsupervised way exploiting Hebbian-like learning mechanisms. …”
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A Novel Ensemble Classifier Selection Method for Software Defect Prediction by Xin Dong, Jie Wang, Yan Liang

“…The experimental results demonstrate that the DFD ensemble learning-based software defect prediction model outperforms the ten other models, including five common machine learning (ML) classification algorithms (logistic regression (LR), naïve Bayes (NB), K-nearest neighbor (KNN), decision tree (DT), and support vector machine (SVM)), two deep learning (DL) algorithms (multi-layer perceptron (MLP) and convolutional neural network (CNN)), and three ensemble learning algorithms (random forest (RF), extreme gradient boosting (XGB), and stacking). …”
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IAE-CDNet: A Remote Sensing Change Detection Network for Buildings With Interactive Attention-Enhanced by Zhaoyang Han, Linlin Zhang, Qingyan Meng, Chongchang Wang, Wenxu Shi, Maofan Zhao

“…Currently, the development of deep learning has had a positive impact on remote sensing image change detection tasks, but many current methods still face challenges in effectively processing global and local features, especially in the task of building change detection in high-resolution images containing complex scenes. …”
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Prediction of mechanical characteristics of shearer intelligent cables under bending conditions. by Lijuan Zhao, Dongyang Wang, Guocong Lin, Shuo Tian, Hongqiang Zhang, Yadong Wang

“…The research shows that the TCN-BiLSTM-SEAttention model demonstrates outstanding predictive capability under complex operating conditions, providing a novel approach for improving cable management and equipment safety through optical fiber monitoring technology in the intelligent development of coal mines, highlighting the potential of deep learning in complex mechanical predictions.…”
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Soft-Label Supervised Meta-Model with Adversarial Samples for Uncertainty Quantification by Kyle Lucke, Aleksandar Vakanski, Min Xian

“…Despite the recent success of deep-learning models, traditional models are overconfident and poorly calibrated. …”
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MMFW-UAV dataset: multi-sensor and multi-view fixed-wing UAV dataset for air-to-air vision tasks by Yang Liu, Zhihao Sun, Lele Xi, Lele Zhang, Wei Dong, Chen Chen, Maobin Lu, Hailing Fu, Fang Deng

“…Several mainstream deep learning-based object detection architectures are evaluated on MMFW-UAV and the experimental results demonstrate that MMFW-UAV can be utilized for fixed-wing UAV identification, detection, and monitoring. …”
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Technology applications in bovine gait analysis: A scoping review. by Amir Nejati, Anna Bradtmueller, Elise Shepley, Elsa Vasseur

“…Among emergent technologies, deep learning and wearable sensors (e.g., accelerometers) appear to be the most promising options. …”
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Attention-Based CNN Fusion Model for Emotion Recognition During Walking Using Discrete Wavelet Transform on EEG and Inertial Signals by Yan Zhao, Ming Guo, Xiangyong Chen, Jianqiang Sun, Jianlong Qiu

“…Accurate recognition of emotion is achieved by training in an end-to-end deep learning fashion and taking into account multi-modal fusion. …”
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A Novel Long Short-Term Memory Predicted Algorithm for BDS Short-Term Satellite Clock Offsets by Tailai Wen, Gang Ou, Xiaomei Tang, Pengyu Zhang, Pengcheng Wang

“…To deal with this problem, we proposed a deep learning-based approach for BDS short-term satellite clock offset modeling which utilizes the superiority of Long Short-Term Memory (LSTM) derived from Recurrent Neural Networks (RNN) in time series modeling, and we call it QPLSTM. …”
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Applications of Gene Expression Programming for Estimating Compressive Strength of High-Strength Concrete by Fahid Aslam, Furqan Farooq, Muhammad Nasir Amin, Kaffayatullah Khan, Abdul Waheed, Arslan Akbar, Muhammad Faisal Javed, Rayed Alyousef, Hisham Alabdulijabbar

“…Moreover, variable intensity and correlation have shown that deep learning can be used to know the exact amount of materials in civil engineering rather than doing experimental work. …”
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CenterNet-Elite: A Small Object Detection Model for Driving Scenario by Lingling Wang, Xiang Li, Xiaoyan Chen, Bin Zhou

“…With the rapid development of deep learning networks, the accuracy of generic object detection has consistently improved. …”
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Fast 3D Transmission Tower Detection Based on Virtual Views by Liwei Zhou, Jiaying Tan, Jing Fu, Guiwei Shao

“…However, traditional 3D target detection techniques, which involve searching the entire 3D space, are marred by low accuracy and high computational demands. Although deep learning-based 3D target detection methods have significantly improved detection precision, they rely on a large volume of 3D target samples for training and are sensitive to point cloud data density. …”
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Masked Modeling-Based Ultrasound Image Classification via Self-Supervised Learning by Kele Xu, Kang You, Boqing Zhu, Ming Feng, Dawei Feng, Cheng Yang

“…Recently, deep learning-based methods have emerged as the preferred approach for ultrasound data analysis. …”
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Learning to Boost the Performance of Stable Nonlinear Systems by Luca Furieri, Clara Lucia Galimberti, Giancarlo Ferrari-Trecate

“…However, maintaining closed-loop stability while boosting the performance of nonlinear control systems using data-driven and deep-learning approaches stands as an important unsolved challenge. …”
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Deep empirical neural network for optical phase retrieval over a scattering medium by Huaisheng Tu, Haotian Liu, Tuqiang Pan, Wuping Xie, Zihao Ma, Fan Zhang, Pengbai Xu, Leiming Wu, Ou Xu, Yi Xu, Yuwen Qin

“…The DENN might shed new light on the applications of deep learning in physics, information science, biology, chemistry and beyond.…”
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Bilingual hate speech detection on social media: Amharic and Afaan Oromo by Teshome Mulugeta Ababu, Michael Melese Woldeyohannis, Emuye Bawoke Getaneh

“…In this work, a Bilingual hate speech detection for Amharic and Afaan Oromo languages were conducted using four different deep learning classifiers (CNN, BiLSTM, CNN-BiLSTM, and BiGRU) and three feature extraction (Keras word embedding, word2vec, and FastText) techniques. …”
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Eigen Solution of Neural Networks and Its Application in Prediction and Analysis of Controller Parameters of Grinding Robot in Complex Environments by Shixi Tang, Jinan Gu, Keming Tang, Wei Ding, Zhengyang Shang

“…Lastly, to verify the eigen solution theory of neural networks, the proposed theory was applied to a novel prediction and analysis model of controller parameters of grinding robot in complex environments with deep neural networks and then build prediction model with deep learning neural networks for controller parameters of grinding robot. …”
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