DMM-YOLO: A high efficiency soil fauna detection model based on an adaptive dynamic shuffle mechanism by Jiehui Ke, Renbo Luo, Guoliang Xu, Yuna Tan, Zhifeng Wu, Liufeng Xiao

“…Additionally, different efficient modules with multi-branch fusion structures, integrating DLSConv, are adopted for the Backbone and Neck to enhance feature extraction and fusion, while optimizing the feature maps fed into the detection head, thereby improving the network’s ability to extract features and detect targets. …”
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Deep learning-based surface deformation tracking with interferometric fringes: A case study in Taiwan by Shih-Teng Chang, Shih-Yuan Lin, Yu-Ching Lin

“…By enabling deformation detection across different magnitudes, time periods, and regions, the proposed framework offers a scalable and transferable solution for extending MT-InSAR-based surface hazard tracking.…”
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Toward AI-Enabled Approach for Urdu Text Recognition: A Legacy for Urdu Image Apprehension by Kamlesh Narwani, Hongzhi Lin, Sandeep Pirbhulal, Mir Hassan

“…It covers a wide variety of text images with both Nastaleeq and Naskh writing styles, taken from different streets and roads of Pakistan. The vast diversity of this dataset makes it a benchmark to work on and train robust neural networks for the detection and recognition of cursive text. …”
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Research on the UAV Sound Recognition Method Based on Frequency Band Feature Extraction by Jilong Zhong, Aigen Fan, Kuangang Fan, Wenjie Pan, Lu Zeng

“…This method replaces the Mel filter in the classic algorithm with a piecewise linear function with the frequency band weight as the slope, which can effectively suppress the influence of low- and high-frequency noise and fully focus on the different frequency band feature data of UAV sounds. …”
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Coordinate Attention Filtering Depth-Feature Guide Cross-Modal Fusion RGB-Depth Salient Object Detection by Lingbing Meng, Mengya Yuan, Xuehan Shi, Qingqing Liu, Le Zhange, Jinhua Wu, Ping Dai, Fei Cheng

“…Many methods use the same feature interaction module to fuse RGB and depth maps, which ignores the inherent properties of different modalities. In contrast to previous methods, this paper proposes a novel RGB-D salient object detection method that uses a depth-feature guide cross-modal fusion module based on the properties of RGB and depth maps. …”
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A robust deep learning approach for segmenting cortical and trabecular bone from 3D high resolution µCT scans of mouse bone by Amine Lagzouli, Peter Pivonka, David M. L. Cooper, Vittorio Sansalone, Alice Othmani

“…We trained DBAHNet on a limited dataset of 3D µCT scans of mouse tibiae and evaluated its performance on a diverse dataset collected from seven different research studies. This evaluation covered variations in resolutions, ages, mouse strains, drug treatments, surgical procedures, and mechanical loading. …”
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Drainage Pipeline Multi-Defect Segmentation Assisted by Multiple Attention for Sonar Images by Qilin Jin, Qingbang Han, Jianhua Qian, Liujia Sun, Kao Ge, Jiayu Xia

“…The multiple attention method proposed in this paper was adopted for detection, instance segmentation, and pose detection in different public datasets, especially in the object detection of the coco128-seg dataset under the same condition. …”
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Lithium-Ion Battery State of Health Degradation Prediction Using Deep Learning Approaches by Talal Alharbi, Muhammad Umair, Abdulelah Alharbi

“…Obtained results shows that the highest testing RMSE (0.666) and MAPE (0.980) are observed during decentralized learning, while the centralized approach shows varying performance across different batteries. The decentralized approach effectively balances performance and privacy, highlighting the reliability of federated learning in SoH prediction for lithium-ion batteries.…”
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Multiscale Edge Enhancement and Progressive Change-Aware Network for Remote Sensing Change Detection by Yan Xing, Jiali Hu, Yunan Jia, Rui Huang

“…In addition, we propose a progressive change-aware module that progressively applies depthwise separable convolutions with kernels of decreasing size to localize changes at different scales, enabling precise refinement of change objects and reducing pseudochanges. …”
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Recent Advances in Deep Learning-Based Spatiotemporal Fusion Methods for Remote Sensing Images by Zilong Lian, Yulin Zhan, Wenhao Zhang, Zhangjie Wang, Wenbo Liu, Xuhan Huang

“…Consequently, spatiotemporal fusion techniques, which integrate images from different sensors, have garnered significant attention. …”
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Detecting Fake News in Urdu Language Using Machine Learning, Deep Learning, and Large Language Model-Based Approaches by Muhammad Shoaib Farooq, Syed Muhammad Asadullah Gilani, Muhammad Faraz Manzoor, Momina Shaheen

“…The research uses methods that look at the features of documents and classes to detect fake news in Urdu. Different models were tested, including machine learning models like Naïve Bayes and Support Vector Machine (SVM), as well as deep learning models like Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM), which used embedding techniques. …”
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Application of deep learning models in gastric cancer pathology image analysis: a systematic scoping review by Sijun Xia, Yuanze Xia, Ting Liu, Yiming Luo, Patrick Cheong-Iao Pang

“…The applicability to different types and stages of GC is also unclear. Conclusions Future research must build larger, more diverse, and more representative datasets. …”
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LUNA: Loss-Construct Unsupervised Network Adjustment for Low-Dose CT Image Reconstruction by Ritu Gothwal, Shailendra Tiwari, Shivendra Shivani

“…The optimal update of the network depends on the various loss functions. Different loss functions, including perceptual loss, SSIM loss, WL2 loss, WTV loss, and sinogram loss, are used to guide the overall reconstruction. …”
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Transformer-Based Optimization for Text-to-Gloss in Low-Resource Neural Machine Translation by Younes Ouargani, Noussaim El Khattabi

“…The trials involve optimizing a minimal model, and our complex model with different optimizers; The findings from these trials show that both Adaptive Gradient (AdaGrad) and Adaptive Momentum (Adam) offer significantly better performance than Stochastic Gradient Descent (SGD) and Adaptive Delta (AdaDelta) in the minimal model scenario, however, Adam offers significantly better performance in the complex model optimization task. …”
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A review of machine learning and deep learning for Parkinson’s disease detection by Hajar Rabie, Moulay A. Akhloufi

“…Our evaluation included different algorithms such as support vector machines (SVM), random forests (RF), convolutional neural networks (CNN). …”
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Managing Timing Uncertainties in Worst-Case Design of Machine Learning Applications by Robin Hapka, Rolf Ernst

“…., robot-human collaboration using convolutional neural networks, timing must be considered to operate safely. …”
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ABDviaMSIFAT: Abnormal Crowd Behavior Detection Utilizing a Multi-Source Information Fusion Technique by Ali Ahmad Hamid, S. Amirhassan Monadjemi, Bijan Shoushtarian

“…To solve this issue, we suggest a new method that combines data from various sources with different characteristics to enhance the precision of detecting human behavior in crowds. …”
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Recent Advancement in Postharvest Loss Mitigation and Quality Management of Fruits and Vegetables Using Machine Learning Frameworks by Abha Singh, Gayatri Vaidya, Vishal Jagota, Daniel Amoako Darko, Ravindra Kumar Agarwal, Sandip Debnath, Erich Potrich

“…It is especially important when evaluating crops at different phases of harvest and postharvest. Crop disease and damage detection is a high-priority activity because some postharvest diseases or damages, such as decay, can destroy crops and produce poisons that are toxic to humans. …”
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Development of Deep Learning Model for the Recognition of Cracks on Concrete Surfaces by Tien-Thinh Le, Van-Hai Nguyen, Minh Vuong Le

“…It is also confirmed that the developed DL-based model was robust and efficient, as it can take into account different conditions on the concrete surfaces. The CNN model developed in this study was compared with other works in the literature, showing that the CNN model could improve the accuracy of image classification, in comparison with previously published results. …”
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A Health Status Identification Method for Rotating Machinery Based on Multimodal Joint Representation Learning and a Residual Neural Network by Xiangang Cao, Kexin Shi

“…Second, an orthogonal projection combined with a Transformer is used to enhance the target modality, while a modality attention mechanism is introduced to take into consideration the interaction between different modalities, enabling multimodal fusion. Finally, the fused features are input into a residual neural network (ResNet) for health status identification. …”
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