Automatic detection of developmental stages of molar teeth with deep learning by Ertuğrul Furkan Savaştaer, Berrin Çelik, Mahmut Emin Çelik

“…The stages of development of molar teeth were divided into 4 classes such as M1, M2, M3 and M4. 9 different convolutional neural network models, which were Cascade R-CNN, YOLOv3, Hybrid Task Cascade(HTC), DetectorRS, SSD, EfficientNet, NAS-FPN, Deformable DETR and Probabilistic Anchor Assignment(PAA), were used for automatic detection of these classes. …”
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MAS-YOLOv11: An Improved Underwater Object Detection Algorithm Based on YOLOv11 by Yang Luo, Aiping Wu, Qingqing Fu

“…By employing learnable spatial weighting parameters, ASFFHead adaptively fuses features across different scales, significantly improving the robustness of multi-scale object detection. …”
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A 3-dimension urban growth analysis using local climate zone mapping by Haojie Chen, Cheolhee Yoo, Jacqueline T. Y. Lo

“…This study proposes an approach to characterize a city’s volumetric expansion for exploring urban land changes based on Local Climate Zone (LCZ) mapping by using a new convolutional neural network (CNN) model termed as DenseNetLCZ. …”
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Comparative analysis of the DCNN and HFCNN Based Computerized detection of liver cancer by Sandeep Dwarkanth Pande, Pala Kalyani, S Nagendram, Ala Saleh Alluhaidan, G Harish Babu, Sk Hasane Ahammad, Vivek Kumar Pandey, G Sridevi, Abhinav Kumar, Ebenezer Bonyah

“…This study compares two frameworks, Deep Convolutional Neural Network (DCNN) and Hierarchical Fusion Convolutional Neural Networks (HFCNN), to assess their effectiveness in liver cancer segmentation. …”
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Practical guidelines for cell segmentation models under optical aberrations in microscopy by Boyuan Peng, Jiaju Chen, P. Bilha Githinji, Ijaz Gul, Qihui Ye, Minjiang Chen, Peiwu Qin, Xingru Huang, Chenggang Yan, Dongmei Yu, Jiansong Ji, Zhenglin Chen

“…We train and test several segmentation models, including the Otsu threshold method and Mask R-CNN with different network heads (FPN, C3) and backbones (ResNet, VGG, Swin Transformer), under aberrated conditions. …”
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Determining optimal strategies for primary prevention of cardiovascular disease: a synopsis of an evidence synthesis study by Olalekan A Uthman, Lena Al-Khudairy, Chidozie Nduka, Rachel Court, Jodie Enderby, Seun Anjorin, Hema Mistry, G J Melendez-Torres, Sian Taylor-Phillips, Aileen Clarke

“…More research directly comparing different strategies and looking at long-term, real-world effects is needed.…”
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Multi-scale CNN-CrossViT network for offline handwritten signature recognition and verification by Wanying Li, Mahpirat Muhammat, Xuebin Xu, Alimjan Aysa, Kurban Ubul

“…Abstract Developing technologies that can accurately identify and highlight subtle differences in signatures is crucial for improving the performance of signature recognition and verification. …”
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Towards the Development of the Clinical Decision Support System for the Identification of Respiration Diseases via Lung Sound Classification Using 1D-CNN by Syed Waqad Ali, Muhammad Munaf Rashid, Muhammad Uzair Yousuf, Sarmad Shams, Muhammad Asif, Muhammad Rehan, Ikram Din Ujjan

“…This study presents a Clinical Decision Support System (CDSS) for the early detection of respiratory disorders using a one-dimensional convolutional neural network (1D-CNN) model. The ICBHI 2017 Breathing Sound Database, which contains samples of different breathing sounds, was used in this research. …”
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Detecting Anomalies in Hydraulically Adjusted Servomotors Based on a Multi-Scale One-Dimensional Residual Neural Network and GA-SVDD by Xukang Yang, Anqi Jiang, Wanlu Jiang, Yonghui Zhao, Enyu Tang, Zhiqian Qi

“…Firstly, the multi-scale features of the vibration signals of the hydraulically adjusted servomotor were extracted and fused using one-dimensional convolutional blocks with three different scales to construct a multi-scale one-dimensional residual neural network binary classification model capable of recognizing normal and abnormal states. …”
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Dynamic spatiotemporal graph network for traffic accident risk prediction by Pengcheng Zhang, Wen Yi, Yongze Song, Penggao Yan, Peng Wu, Ammar Shemery, Keith Hampson, Albert P. C. Chan

“…However, predicting traffic accident risks is challenging due to the relationships among factors such as weather, traffic conditions, and road characteristics, along with capturing spatial correlations of traffic accidents across different time scales. To address these challenges, we propose the dynamic spatial-temporal accident risk network (DSTAR-Net). …”
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Identification of Subtypes of Post-Stroke and Neurotypical Gait Behaviors Using Neural Network Analysis of Gait Cycle Kinematics by Andrian Kuch, Nicolas Schweighofer, James M. Finley, Alison McKenzie, Yuxin Wen, Natalia Sanchez

“…We first trained a Convolutional Neural Network and a Temporal Convolutional Network to extract features that distinguish impaired from neurotypical gait. …”
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A Novel Spatio–Temporal Deep Learning Vehicle Turns Detection Scheme Using GPS-Only Data by Mussadiq Abdul Rahim, Sultan Daud Khan, Salabat Khan, Muhammad Rashid, Rafi Ullah, Hanan Tariq, Stanislaw Czapp

“…It outperforms the existing turn detection schemes on two major frontiers, the required data and the accuracy achieved in detecting different driving behaviors.…”
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Fault Diagnosis Method for Main Pump Motor Shielding Sleeve Based on Attention Mechanism and Multi-Source Data Fusion by Nengqing Liu, Xuewei Xiang, Hui Li, Zhi Chen, Peng Jiang

“…Then, on the basis of the AM-MSCNN, a convolutional neural network structure based on the attention mechanism for multi-scale and multi-source data fusion (AM-MSMDF-CNN) is proposed to further fuse the primary fusion features of different channels of torque, rotational speed, voltage, and current. …”
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Integrating UAV-Based Multispectral Data and Transfer Learning for Soil Moisture Prediction in the Black Soil Region of Northeast China by Tong Zhou, Shoutian Ma, Tianyu Liu, Shuihong Yao, Shenglin Li, Yang Gao

“…The transferability of these models across different regions remains a considerable challenge. …”
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Multi-fusion strategy network-guided cancer subtypes discovering based on multi-omics data by Jian Liu, Xinzheng Xue, Pengbo Wen, Qian Song, Jun Yao, Shuguang Ge

“…Through comprehensive analysis and in-depth analysis of the genomic data of a large number of cancer patients, researchers can more accurately identify different cancer subtypes and reveal their molecular heterogeneity.MethodsIn this paper, we propose the SMMSN (Self-supervised Multi-fusion Strategy Network) model for the discovery of cancer subtypes. …”
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Starting driving style recognition of electric city bus based on deep learning and CAN data by Dengfeng Zhao, Zhijun Fu, Chaohui Liu, Junjian Hou, Shesen Dong, Yudong Zhong

“…The starting driving style recognition method based on Convolutional Neural Network (CNN) model is constructed. …”
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Infrared Aircraft Detection Algorithm Based on High-Resolution Feature-Enhanced Semantic Segmentation Network by Gang Liu, Jiangtao Xi, Chao Ma, Huixiang Chen

“…Firstly, the designed location attention mechanism is utilized to enhance the current-level feature map by obtaining correlation weights between pixels at different positions. Then, it is fused with the high-level feature map rich in semantic features to construct a location attention feature fusion network, thereby enhancing the representation capability of target features. …”
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Multi-Head Graph Attention Adversarial Autoencoder Network for Unsupervised Change Detection Using Heterogeneous Remote Sensing Images by Meng Jia, Xiangyu Lou, Zhiqiang Zhao, Xiaofeng Lu, Zhenghao Shi

“…Heterogeneous remote sensing images, acquired from different sensors, exhibit significant variations in data structure, resolution, and radiometric characteristics. …”
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MLHI-Net: multi-level hybrid lightweight water body segmentation network for urban shoreline detection by Jianhua Ye, Pan Li, Yunda Zhang, Ze Guo, Shoujin Zeng, Youji Zhan

“…Moreover, they are also incapable of accurately extracting fuzzy boundaries caused by different scenes and climatic conditions. To address these challenges, this paper proposes a multi-level hybrid lightweight water segmentation network, MLHI-Net. …”
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Calibrating calving parameterizations using graph neural network emulators: application to Helheim Glacier, East Greenland by Y. Koo, Y. Koo, Y. Koo, G. Cheng, M. Morlighem, M. Rahnemoonfar, M. Rahnemoonfar

“…When these GNNs are trained with numerical simulations of Helheim Glacier, Greenland, for different calving stress thresholds, they successfully reproduce the observed evolution of ice velocity, ice thickness, and ice front migration between 2007 and 2020. …”
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